System and method for standardizing and tracking land use utility

ABSTRACT

Property records and parcel boundaries form the basis for much of the backbone used in the real estate industry for a myriad of activities from property search, analysis, evaluations for lending etc. However, these records do not include accurate and consistent data for fields related to a property&#39;s zoning or possible future land use. This invention targets transforming disparate data sources needed to express a property&#39;s land use utility and standardizes the multiple records into a standardized land use utility with an element for time that allows tracking and measuring changes in standardized land use utility at the parcel level for one or more locations. Improvements in this invention are directed to an improvement in computer-related technology.

RELATED PATENT APPLICATIONS AND INCORPORATION BY REFERENCE

This is a Continuation in Part application based upon utility patent application Ser. No. 16/127,938 filed on Sep. 11, 2018 named “Methods for Transforming Complex Zoning Codes and Regulations to Produce Usable Data”, and patent application Ser. No. 14/319,937 filed on Jun. 30, 2014, now U.S. Pat. No. 10,074,145 B2 and patent application Ser. No. 12/873,267 filed on Aug. 31, 2010, now U.S. Pat. No. 8,765,855 B2, which claim the benefit and priority date from provisional application No. 61/238,613 filed on Aug. 31, 2009. These related patent applications are incorporated herein by reference as if restated in their entirety. This Continuation in Part application claims the priority dates of the related patent applications.

BACKGROUND OF THE INVENTION (1) Field of the Invention

The present invention relates to the field of real property development and utility. More specifically, the embodiments relate to computer enabled search of structured data with business logic and business methods specific to the complex subject of zoning and land-use development controls. The invention generally relates to the development of real property. More particularly, the invention relates to creating data sets pertaining to particular parcels of real property to provide property development potential and use. Results can be tailored to reflect the particular needs of potential purchasers, sellers, renters, planners, economic developers, remodelers or real estate developers of property based on their role, motivation and experience.

In this most recent invention, property records include descriptions for the property's current use but there is no way for a person review such a record to understand that current property use as it relates to the property's zoning or possibly future land use. Furthermore, property records and parcel boundaries form the basis for much of the backbone used in the real estate industry for a myriad of activities from property search, analysis, evaluations for lending etc. This invention targets transforming disparate data sources needed to express a property's utility and standardizes them to create a standardized land use utility with an element for time that allows tracking and measuring changes in land use utility for one or more locations. Improvements in this invention are directed to an improvement in computer-related technology.

(2) Related Art

The known related art comprises printed publications disclosing zoning regulations, municipal regulations, covenants, conditions and restrictions (referred to as CC&Rs), and maps showing zoning. The known related art fails to make zoning information and its impacts readily accessible to a lay person attempting to engage in real estate acquisition, remodeling, understanding a proposed new development, neighborhood assessments, real estate development or other land use studies, future planning and financial consequences for decisions.

In the related art of applications using zoning, the related art looks at the subject of zoning as a code to optionally decipher through manual processes. U.S. Pat. No. 7,881,948 (Continuation of U.S. Pat. No. 7,346,519) by Carr, discloses a location standardization invention in which “zoning” is a field for providing the abbreviated name and/or name without any attempt to explain the meaning of the zoning controls.

U.S. Pat. No. 20050273346 by Frost is real property information management system in which zoning maps from a city can be viewed, and the user can manually determine the zoning abbreviation associated with a property with a descriptor.

U.S. Pat. No. 7,389,242 by Frost, incorporates zoning as part of a shared document process for professionals and allows for data about a requested zoning district to be uploaded by a governmental entity service provider into a user interface.

U.S. Patent No. 20130036031 by Hutchinson incorporates zoning as part of its system for a building permit system. While zoning is identified as one of several data elements, it is focus on expediting permitting for a project being considered rather than provide a method to standardize current zoning, future land use or changed zoning, through the use of descriptors to transform data into useful data.

Prior artwork has focused on the zoning district abbreviation or district name without providing the rules or controls associated with the zoning district. Furthermore, prior artwork does not associate the impacts of current and proposed future zoning controls on one or more properties. Prior artwork treats zoning as if it were a uniform data available in the same format regardless of jurisdiction. That is not the case. Moreover, prior art does not make associations between the zoning data and the zoning ordinance, which are separate. Zoning is more complex a subject than prior art shows.

The present invention relates to systems and methods for standardizing and tracking land use utility. It is generally known in the prior art to provide computer-implemented methods and systems that allow users to aggregate data and visualize whether for creating charts or 3D building massing. The following prior art patent documents that appear to be closest to being relevant for this patent application include the following:

U.S. patent application Ser. No. 16/789,681 Gridics LLC has a patent for a system and method actual density capacity and including a description for using uses allowed by zoning as part of its system.

U.S. patent application Ser. No. 13/473,137 Signty is focused on calculations with an emphasis on using zoning to create a building massing which focuses on development standards within a zoning code.

U.S. application Ser. No. 14/644,948 Jones Lang LaSalle IP Inc. describes a system and method for visualizing real estate markets and uses a classification of raw data and normalizes data.

The invention is being further improved with the discovery of standardizing current property use with standardizing zoning and/or land use to a land use utility classifier list and/or subcategory classifier list with data sets being dated to allow for tracking data over time. By standardizing multiple data sets with consideration for time, several innovations emerge: 1. True market land supply is now available with options to filter data a number of way. 2. Arbitrage opportunities based on differences between current property use and zoning and/or between current zoning and future land use are available. 3. Both micro and macro analysis on land supply by economists, data scientist and policy analysis is feasible. 4. Standardized land use utility solves a problem between “multifamily” per zoning which usually uses three (3+) units in contrast to the real estate industry lending standards of four (4+) for residential and five (5+) for commercial. 5. Enhanced search capabilities by land use utility. 6. Enhanced existing property use record information. This invention's claims are directed to an improvement in computer-related technology.

BRIEF SUMMARY OF THE INVENTION

The present invention overcomes shortfalls in the related art by presenting an unobvious and unique combination, configuration and manipulation of real estate related data sets, user defined criteria, and other sets of data. The present invention overcomes shortfalls in the known related art by, inter alia, presenting unique user interfaces, search methods, data structures and informational paradigms to assist a person in deciphering complex zoning codes and regulations. The invention presents unobvious combinations of components, search methods data organization and data transformation. The present invention overcomes shortfalls in the known related art by, inter alia, presenting unique user interfaces, search methods, data structures and informational paradigms to assist a person in recognizing the impacts of zoning codes and regulations including the ability to run searches using hypothetical scenarios mimicking the zoning process. The invention connects “cause” comprising the zoning controls and the zoning process to hypothetically change a control with “effect” from both a physical and economic view comprising building size, number of units, building height, changes in demographics, transportation options, retail sales and employment opportunities. It also allows for improved communication between the general population and the proposed and planned developments in their communities. The present invention overcomes shortfalls in the related art by using descriptors to make zoning change more readily understood and further transform zoning controls in such a way as to make zoning data across multiple cities more usable.

Certain embodiments of the invention may serve as a data manipulation and retrieval tool that transforms publicly available zoning ordinance data from cities and counties into a more user-friendly structure by breaking down the data into individual data points and cataloging the formal names using commonly recognized names. Methods to measure the flexibility of a particular zoning code or classification includes a sub-component related to the rating of a city or county's practices as they pertain to the obstacles for obtaining a special use permit, a permit to remodel and the enforcement of their regulations. Method to use structured data to allow an electronic map to convey zoning controls and/or zoning policy objectives by location. Methods to locate suitable properties using a desired use of a property as the primary search criteria for one or more jurisdictions. Methods to measure and test hypothetical scenarios involving zoning and land-use development controls on a subject property, area and/or specified constraint to obtain quantifiable results of development and use constraints associated with zoning case and permit history. Methods to identify zoning compatibility issues for real estate development and/or business use. Methods to allow user to ask questions and retrieve answers relevant to their needs using perspective. Methods to describe zoning controls such as shaping building size. Methods to associate zoning changes with a list of descriptors. Methods to relate changes in zoning regulations with an index.

Certain embodiments of this most recent invention, further transform land use through data standardization from multiple data sources which, taken together, create land use utility. This system and method allows for measuring, search and analysis and does so by the idea of using an existing use from property record and standardizing it to the same classification system used for zoning and/or future zoning. In real estate, location is a fundamental principle. However, as this invention has disclosed the need for translating the complexities inherent in zoning and land use to make the subject accessible to the layperson. Zoning is an intangible when buying or renting a property; it is a complex subject difficult to grasp by many home owners, investors and agents. Many use the “existing use” as a proxy for land use utility without knowing that the dirt gets zoned. This invention makes that evident by the system identifying land use from one or more sources to create land use utility. The combination of current or existing property use, current zoning and/or future land use—all standardized—offers a complete solution to the problem that an existing use may no longer meet the zoning and/or the future land use may aspire to a different use than the current use. Furthermore, this invention creates a new way to analyze supply of land vs. existing building stock as it creates a complete view of the market by category and/or market over time. Search is also able to effectively uncover properties with more intense land use than the current property record indicates. It also provides an innovative way to solve an inherent issue in real estate for “units” as most zoning ordinances use a definition for “three (3) or more units” for defining a multifamily use while the real estate industry uses “one to four” (1-4) to reflect lending standards.

Embodiments of the invention include the following features:

Unique logic developed to form groups of data; visual guideposts to find look up functions using user need, use, size, building codes, addresses and other criteria; reverse direction of information retrieval; and the creation and measurement of a zoning code's flexibility or ease of obtaining conditional use permits, and/or variances.

Further embodiments include the creation of a Zoning Score to measure compatibility of proposed uses to certain sites and/or the conformity of existing improvements on the site and/or level of compatibility of a property's zoning considering adjacent properties; the creation of letters of verification for issuance from a city or county municipality to a user; and the creation of letters from users to a city or county municipality to request clarification or confirmation of a proposed use.

The present invention provides a novel way to search real property data relative to zoning and land-use development controls through a methodic design to address the complex by combining and associating logic for a) the rules b) property data and c) the zoning process.

In one aspect of the invention, the invention provides the user a way to access data from multiple governmental agencies in one interface, associate a descriptor with each district and/or area and search for one or more properties and/or uses and/or zoning districts and/or development control to query the data for better understand of policy impacts.

In another, the capabilities expand to allow the user to search for one or more properties relative to existing and/or potential conditions for a zoning control variable comprising use, building size, building type and/or some combination, to automatically analyze site selection viability for one or more jurisdictions. Results can be mapped, viewed at the parcel level and/or viewed as a group with the ability to apply economic data including zoning permits as a way to further search and/or filter results. Search is the emphasis and includes the pairing of the invention with known technologies such as free text.

In still another aspect of the invention, users can test outputs using hypothetical scenarios for data modeling with zoning actions comprising rezoning, asking for a change in use and/or development standard requirement. Results are paired with economic data, including zoning permits, and hypothetical financial impacts.

The invention takes current processes and known outputs and automates and/or streamlines and/or creates an interactive aspect to services currently only provided by the government planners or private planners. These include but are not limited to letter of verification and/or determination, character area, planning capacity study, existing condition report and/or notification of proposed project and/or outcomes from public hearings regarding zoning cases for use in unofficial capacity.

Unique logic associated with the concept of zoning compatibility in which zoning district permissions for a development or use are supplemented by the need for the location to not be in conflict with identified uses the community protects such as churches and schools. This logic allows for a more targeted search, evaluation of site suitability and planning tool.

Methods to measure the impacts of a flexibility of a particular zoning code or zoning control on a specified property or group of properties relative to hypothetical zoning classification includes a sub-component related to the rating of a city or county's practices as they pertain to the obstacles for obtaining a special use permit, a permit to remodel and the enforcement of their regulations.

In one embodiment of the invention, users may obtain needed information by requesting a specific data element or data grouping. The novel use of spatial data, also referred to as, Geographic Information System (GIS) records may be used to provide a “ballpark” or rough estimate analysis regarding the zoning conformity to a user's proposed use of a property.

Methods to associate descriptors with zoning controls such as building size are such that whether the related zoning ordinance calls for a height in feet, in stories, or both, it is handled through the same process to standardize for the lay person interested in general size such as “mid-rise” rather than an exact figure.

Methods to articulate changes in zoning are more clear because they are designed to include the “when,” “why,” “what” and “who” behind the change with a consistent methodology for the zoning itself as well as for the identified changes by one or more zoning descriptors.

Methods to articulate changes in zoning across multiple cities using an index to measure change in the same consistent manner creates a new way to understand the timelines for zoning changes for more than one city and over multiple periods of time.

In one embodiment of this recent improvement to the invention, the system and method described herein extend the technology used for standardizing zoning and/or future land use by looking for patterns in the data that can be made used—with “truth data”—to better identify optimal land use utility. The invention also includes provisions for incorporating machine learning into the data standardization module. Also, one of the invention's improvements pertains to data outputs ranging from all spatial to tabular to a hybrid which has geographical features with non-spatial attributes and the noted improvement of vector tiles which are “vessels” that contain geometries and metadata.

In contrast to systems and methods of prior art, embodiments of the present invention improve computer technology for search, analysis, tracking, mapping and assessing for land and improved property through standardized land use utility system and method.

Prior art does not build standardization into creating a new data set that offers such extendibility from solving practical problems for the layperson through the use of visualization and icons to the data modelers and economists who prefer data driven dashboards.

Prior art does not solve the translating of “multifamily” from zoning to industry standards with a focus on how properties can financed. Nor does prior art attempt to track changes in land use utility over time by categories and subcategories.

In one embodiment, a system for standardizing land use utility includes a graphical user interface to receive a land use utility query, a database that incorporates multiple data stores, a processor to execute instructions written into the logic and a graphical user interface to display results from the query.

In another embodiment, the present invention includes a method for standardizing land use utility with the steps of receiving a land use utility query through a graphical user interface, accessing a database that incorporates multiple data stores, executing instructions by a processor to standardize and combine data sets as related to an inquiry and displaying results.

What is disclosed is a system and method for standardized land use utility at the parcel level which allows for analysis at various scales of geography or location, a neighborhood, zip code, comparing “comparable properties” used in appraisal and extending that to city-to-city or metro-to-metro comparisons. Because the data standardized include property data, zoning and/or future land use with data sets dated, it is possible to track the standardized data by time.

In one embodiment, a time series creates another new way to consider location and with standardization by land use category, such changes can be viewed by market segment. Furthermore, the invention ties into standardization census demographics and the notion of a standardize land use pattern index. The logic is able to compare standardized land use category and provide an optimal land use utility with the a confidence rating.

In another embodiment, arbitrage opportunity for land use utility based on either zoning and/or future land use is an unexpected outcome of this invention that is made possible because current use is also standardized. Furthermore, subcategories for the standardized land use utility classifier list allow for more granular tracking, search, visualization and comparisons than standardizing at the more general category level and even make for the creating of a standardized lender conformity output. In zoning, properties with three (3) or more dwellings are often called “apartments” or “multifamily” while industry lending standards consider properties with up to four (4) dwellings to be “residential” and are loaned accordingly. With land use utility standardization, the distinctions can be made clear and this will help the real estate industry in identifying, tracking and assessing property using consistent metrics.

The invention describes an optimal standardized land use utility which requires logic to compare land use categories to rank by land use intensity hierarchy. This hierarchy is based on reviewing which standardized land use category is more intense for the properties being compared. For instance, a “residential” land use category is less intense than a “multifamily” land use category. The option to compare other property attributes for comparing one or more nearby parcels in conjunction with identifying an optimal standardized land use utility is means to weigh inherent differences in intensity within the standardized land use category. For instance, when solving for what optimal confidence rating to give to a parcel with a current use that is “residential” but its zoning supports “multifamily” and is identified as an arbitrage opportunity, the option to relay a confidence score includes comparing nearby parcels. For this example, assume parcels A, B and subject with the arbitrage opportunity are similar in lot size. If parcel A returns a standardized “multifamily” category and has a standardized current property use “multifamily” and property data shows 10 apartment units in a 10,000 sf building that is 20 years old and the other closes parcel, parcel B returns a standardized “multifamily” category and has a standardized current property use “multifamily” and property data shows 50 apartment buildings in a 50,000 square foot building that are 2 years old, then the opportunity parcel has a “high level” of confidence that the optimal land use utility is multifamily because a) concentration of other like standardized current and zoning uses and b) direction of intensity is increasing by the number of units and the newer building age. This analysis could further review intensity based on dwelling units per acre, a common term used in measuring density of dwelling units. The optional confidence score relates to the levels of comparisons and can be expressed as a numeric single point of value, range or related qualitative description whereby the description can provide a few descriptive words and/or provide a summary as to the rationality of the logic used.

Another embodiment the patterns of standardized land use utility are described whereby the standardized land use utility is measured and/or tracked for one or more locations. Census data is used because it is a core part of a demographics that can relate to land use. By correlating standardized land use utility with census data, the combination creates a new way to understand impacts of land use policy over time. The option to include other property attributes that express a density or size is extend findings to include standardized land use category with a numeric correlation as an option.

The real estate industry uses property data and records, but these have fallen short in the land use arena because there are standards applied by different governmental agencies for different geographies and companies aggregating such records apply their own standardization for existing use; however, this is only focused on existing property use. This invention is not obvious; given all the improvements in data aggregation land use standardization for current property use relative to zoning for the property and/or relative to the property's future land use does not exist yet is very needed in the market.

This continuation-in-part closes the final gap in building land use knowledge for the layperson, a group identified in the very first patent application for translating zoning. People focus often on the current property use, while zoning and/or future land use are the very characteristics of a property that drive change, valuation and interest level by the market for those knowledgeable.

Real estate as an asset class continues to command a large percentage of our country's economy and yet there is no standardized way to know the supply of the market by segment, until now. Data scientist, policy analysts, economists and real estate developers will benefit by this invention.

Some people will want to understand the standardized land use utility for a specific address and will like they can understand the property and immediate area better. Others will focus on arbitrage opportunities and develop algorithms for optimizing the optimal standardized land use utility for their needs. Those who like dashboards and the ability to filter will benefit from the myriad of ways this data can be viewed.

In an embodiment, query results are pre filtered with the option to customize, including the ability to “draw an area” rather than use a specific known boundary. The invention allows for such use cases. As far as outputs, there several including data in a spatial format, a tabular and/or hybrid like GeoJSON and/or visualizations on electronic maps, vector tiles, draw which require the use of spatial data in the invention as noted. Standardizing land use utility is the groundbreaking framework from which such work within the real estate industry can cascade. Tracking standardization of land use utility be improvements and/or vacant status with change by census is yet another embodiment of this invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a sheet of text demonstrating a cross-reference to another code section. And showing the uses permitted by the zoning district; this is an example of a zoning control

FIG. 2a is a table of code and zoning values.

FIG. 2b is a table of translations.

FIG. 3 is a table of zoning codes and permitted uses.

FIG. 4 is a table of zoning codes and various size requirements. lot

FIG. 5 is a table of zoning code information parsed by lot size restrictions.

FIG. 6 is a collection of tables with an example report of allowed construction for a given set of municipal restrictions.

FIG. 7 is a pseudo screen shot of one disclosed user interface.

FIG. 8 is a pseudo screen shot for the display of size data for a giving zone code.

FIG. 9 is a pseudo screen shot showing a look up of use data for a specified zoning code.

FIG. 10 is a pseudo screen shot showing a look up of specified use for a selected zoning code.

FIG. 11 is a chart demonstrating a Zonability Flexibility Rating and its related elements.

FIG. 12 is a table describing the steps needed to create one embodiment of a Zoning Score.

FIG. 13 is a listing of CC&R restrictions.

FIG. 14 is a pseudo screen shot showing an application program interface (API).

FIG. 15 is a table of information relevant to a set of zoning code rules regarding “size” restrictions.

FIG. 16 is a table of information comparing a set of zoning code rules regarding allowed “uses.”

FIG. 17 is a table of comparison of user entered data to zoning code rules.

FIG. 18 is a pseudo screen shot showing one contemplated use of GIS technology with a user entered address.

FIG. 19a is a pseudo screen shot showing details from a use of GIS technology.

FIG. 19b is a pseudo screen shot showing allowed uses for a selected property.

FIG. 20a is a pseudo screen shot showing a sample selection of data generated from a GIS search.

FIG. 20b is a pseudo screen shot showing a sample selection of data generated from a GIS search that shows the zoning ordinance rules.

FIG. 20c is a pseudo screen shot showing a sample selection of data generated from a GIS search and related outcomes.

FIG. 20d is a pseudo screen shot showing a sample selection of data generated from a GIS search that combines all aspects—the look up by address, the zoning code, the rules and the outcomes.

FIG. 21 is a pseudo screen shot showing a GIS map with a Zoe Analysis.

FIG. 22 is a pseudo screen shot showing a data retrieval option.

FIG. 23 is a pseudo screen shot showing CC&R categories. These pertain to properties with shared common areas such as condominiums and master planned communities.

FIG. 24 is a sample letter generated by one embodiment of the invention.

FIG. 25 is an overview of the computer implemented application

FIG. 26A show the current access to the information this application automates for the consumer, expert and government agency

FIG. 26B shows the Zoning Information Decision Tree used by the application to anticipate the consumer's data needs

FIG. 27 show the use and function of the application

FIG. 28A shows the overview of the system

FIG. 28B shows a detailed view of the system

FIG. 28C shows some of the search options

FIG. 28D shows examples of perspective with role and motivation

FIG. 29 shows the basis of the spatial data structure

FIG. 30A is a view of the types of structured data in the database

FIG. 30B shows a sample output for compatibility logic showing distance-to rules for protected uses and/or districts as per the zoning rules.

FIG. 30C shows another output for compatibility

FIG. 30D shows an example of an address with multiple layers of regulation

FIG. 31 shows an example of a data output, an electronic map

FIG. 32 shows an example of data output, the use of descriptors to associate a zoning district with a zoning control or controls

FIG. 33 shows an example of structuring non-spatial data zoning controls regarding development standards

FIG. 34 shows an example of data output, a dashboard view of a city's zoning with the ability to search because of the structured zoning data

FIG. 35A shows another example of data output, the use of data to show a visual representation of one or more property's height potential

FIG. 35B shows potential with location change indicator data

FIG. 36 shows the use of hypothetical zoning actions to test scenarios.

FIG. 37 shows the application's search capability for one or more uses for one or more jurisdictions

FIG. 38 shows the application's search capability for a specific use with options to tailor the search

FIG. 39A shows the applications search capability by a property type through an image

FIG. 39B shows the applications search capability by a recognizable brand name

FIG. 40A is an example of data output showing a use based search with mapped and listed results

FIG. 40B shows an output with a count of the number of matches

FIG. 40C shows an output for a property and 3 closest neighbors based on a search for the use “hotel”

FIG. 40D is a sample output of calculations providing an estimate for maximum building size and height

FIG. 40E is a sample output of messages and definitions associated with the data associated with one or more parcels comprising districts, uses, business controls

FIG. 40F is a sample output of the property data associated with a parcel used to create meaning to the zoning and land-use development controls

FIG. 40G is a sample of outputs for a specific parcel with visual display of the zoning and land-use development controls

FIG. 40H is a sample output of data in an exportable format

FIG. 41 shows results for hypothetical scenario testing

FIG. 42 shows the use of the invention for future determinations

FIG. 43 shows outlier parcels that potentially cross into two same-level jurisdictions

FIG. 44 shows the invention's data structure applied to other districts besides zoning and land-use development but with real estate impacts such as water districts

FIG. 45 shows sample data outputs based on perspective

FIG. 46 shows an example of a formulated regulation

FIG. 47 shows an example of an output from a parcel with changed zoning

FIG. 48 shows an example of raw GIS zoning data in a visual representation.

FIG. 49 shows an example of raw GIS zoning data from the metadata and reflects a typical level of detail. The label gets associated with the abbreviation of the zoning district as found in the zoning ordinance associated with jurisdiction.

FIG. 50 shows an example of two time period for the same jurisdiction using the method of standardizing the zoning districts to a code focus (focus name) and measuring changes for number of parcels impacted by zoning change as expressed by count and percentage with tracking of the number of zoning districts per code foci and the changes between time periods.

FIG. 51 is an overview of the system including its data storage, logic elements, processors and input/output elements used to transform multiple data sets into a uniform classification system focused on land-use utility.

FIGS. 52A and 52B further details for process described.

FIG. 53 shows the flow for standardizing.

FIG. 54 gives an overview for hierarchy by land use category.

FIG. 55 is a sample output at the parcel level.

FIG. 56A-56C show standardizing by data source: current property use, zoning and/or future land use to the same standardized land use utility classifier list and the option for subcategory specialized utility classifier list.

FIG. 57A-57B show a user interface, a graphical user interface with options.

FIG. 58 shows an output for land segmented for a specific city with land use utility categories outputs with breakouts for current and future whereby zoning and current property use reflect “current” and future land use reflects “future.”

FIG. 59A is an output for a single standardized land use category, “multifamily” with a breakout by metro and showing an additional breakout for “vacant” and “improved.” This is for land area whether by acres and/or parcels by land use utility category.

FIG. 59B is another multiple metro view and shows multiple standardized land use categories.

FIG. 59C is for a single location and shows breakout by category by vacant and improved status for standardized current property use.

FIG. 60 is a view of a map output and the options to convey standardized land utility include map toggles for layers and the option to see a view by “improved or vacant,” by “opportunity” which in this case is seeing standardized “residential” for current property use and “multifamily” per zoning and/or future land use.

FIG. 61 is data visualization in the form of a dashboard. These use multiple filters and have options for data integration.

FIG. 62 is a view of “search” using standardized land use utility and includes a subcategory element.

FIG. 63 is a summary of the invention and shows the inherent concept of time through the standardization of land use and this allows for search, tracking, assessing and analyzing land use utility for one or more locations.

REFERENCE NUMBERS

-   -   1 a self-service inquiry component to ask the user what they         want to know comprising the type of query, their perspective and         motivation     -   2 the web application invention runs the queries on a structured         database with logic and returns answers     -   3 are the answers to property utility comprising potential         development and use returned with the user in a number of         formats     -   4 is the database which stores structured spatial and         non-spatial data     -   5 is the logic used to run the queries     -   6 is spatial data obtained from third parties or created     -   7 is zoning controls obtained from the zoning ordinance         comprising such attributes as development standards, use         permissions, policy goals     -   8 is environmental controls and span from watersheds, protected         species, stormwater management, tree protection     -   9 relates to transportation which drives zoning and land-use         development control policy for areas based on zoning attributes         such as street type, transit-oriented developments, traffic         impact analysis tied to what uses are allowed     -   10 is the neighborhood level data comprising of boundaries,         businesses, permits     -   11 is property data usually associated with the local county         assessor comprising of lot size, building size, ownership, year         built, existing use, assessed value.     -   12 is historic related data and comprises districts, landmarks     -   13 is economic related data comprising demographics, retail         sales, real estate sales, rental rates     -   14 use logic to structure zoning control rules based on         compatibility as defined by zoning which often restricts the         development for a commercial building when adjacent to a         residential with the restrictions varying by local     -   15 uses logic to create calculations such as maximium potential         building size, height, density, lot coverage and conformity to         current local zoning rules     -   16 is an output in the form of a report     -   17 is an output in the form of a score     -   18 is an output in the form of an API, application programming         interface     -   19 is an output in the form of an electronic map     -   20 is an output in the form of data     -   21 is the consumer and/or user of the application     -   22 is the current ways the consumer can access the information         this invention gathers automatically for un-official use such as         calling or visiting the local government planning department or         using their published online website documents and maps     -   23 is the government agencies controlling the compliance and         regulation of zoning and land-use development controls     -   24 represents the experts with technical zoning and land-use         development experience     -   25 are the regulations associated with one or more properties         that control the utility and development of real property     -   26 is the government could opt to use the invention to share         across multiple agencies     -   27 is the ability for the application to connect experts with         educated consumers who wish their expertise     -   28 is the ability for government to use the invention to         automate tasks comprising letters of verification and/or letters         of determination, character area analyses and planning capacity         studies.     -   29 is the ability for experts to work with government agencies;         this includes quasi-governmental agencies such as economic         development corporations.     -   30 is the questions the user has presented in the form of search     -   31 is the perspective the user may provide to tailor search         results; perspective includes role (owner, seller, agent),         motivation linked to use of the property     -   32 is the data record created by the query     -   33 represents the format outputs     -   34 represents the combinations possible for search     -   35 is for a property comprising a single parcel or group,         neighborhood or district inquiry with a known location but the         utility of the property is to be found     -   36 use inquiry in which the use comprising businesses, building         types, brand and activities are known but the location or         locations need to be found     -   37 is a search for hypothetical scenarios to test new zoning         controls applied to one or more parcels     -   38 allows for attribute search as related to zoning and land-use         development controls and comprise development standards         (building height, floor area ratio, setback, parking), district         use permissions (business uses, building types, activities),         district policy goals, jurisdiction rebuilding damaged structure         permissions, zoning district abbreviations     -   39 is an electronic map based search using a map     -   40 shows examples of spatial data used in the application     -   41 shows examples of non-spatial data which takes the form of         zoning controls for data related to the zoning ordinance         (district names, definitions, development standards, district         use permissions) and location change indicator data such as         demographics, retail sales, permits     -   42 is the option for the user to iterate with the data returns     -   43 is the option for the user to filter the data returns     -   44 is the option to edit report returns     -   45 represents an output in the form of follow up inquiries     -   46 is information without regard to consistency     -   47 is a test of conformance for a property relative to the         zoning controls     -   48 is zoning information from an official government agency         signed to attest to its warranty     -   49 is unofficial zoning information from a non-governmental         agency     -   50 is a zoning action comprising rezoning, changing a use         permission, changing a development standard     -   51 is a measurement of likelihood regarding a zoning action     -   52 is rating of likelihood low, moderate and high     -   53 is a specific location     -   54 is a district or districts referring to zoning districts,         planned areas or any boundary constraint used to articulate a         zoning and/or land-use development control     -   55 is neighborhood     -   56 is a specific use as found in a local zoning ordinance     -   57 is an existing use     -   58 is a proposed use     -   59 is property utility     -   60 is a development standard control building size use because         parking requirements control use     -   61 is definitions     -   62 is building size potential     -   63 is building height     -   64 is number of units or density     -   65 is investment potential     -   66 is adaptive reuse     -   67 is location change indicator data     -   68 is role buyer     -   69 is owner     -   70 is business owner     -   71 is architect     -   72 is investor     -   73 is lender     -   74 is neighbor     -   75 is neighboring properties     -   76 is color to associate location with zoning control     -   77 is a descriptor used to articulate a zoning control     -   78 is a development standard zoning control     -   79 is organized structured zoning ordinance data creating an         inventory view     -   80 is building information modeling (BIM)     -   81 is standardizing uses from a zoning ordinance to other         classification systems     -   82 shows an interface to select a use     -   83 shows the ability to incorporate a proposed change to reflect         in the data returns; this is also referred to a motivation     -   84 is the ability to search for business operations as         controlled by the zoning     -   85 is the property search by an image     -   86 is the search by brand     -   87 is the visual display of returns matching criteria that when         clicked can generate calculations and retrieve data specific to         the location and based on the query     -   88 is the option to share the findings whether by email, through         a portal on a website or a data export function     -   89 is the map legend able to use color tiles on the map to         express zoning controls     -   90 is a filter for a geography reflecting the data returns         lat/lon coordinates relative to a geo-boundary     -   92 is the “i” for information to add guidance to the user while         in the application     -   93 is the user's preferences for data to be retrieved as part of         a search     -   94 is the search parameter     -   95 is a return of matches based on query     -   96 is the functionality of the list of data record returns from         the query     -   97 numbers the data record returns from the query and when         clicked connects to individual parcel level data returns         automatically     -   98 is the association of zoning permission for a use; “likely         ok” is associated with the zoning ordinance showing the use as         permitted.     -   99 is the association of zoning permission for a use; “maybe ok”         is associated with the zoning ordinance showing the use as         conditional     -   100 the association of zoning permission for a use; “unlikely         ok” is associated with the zoning ordinance showing the use as         prohibited     -   101 is the retrieval of use opportunity for a parcel and its         neighboring parcels     -   102 shows the application's ability to calculate a maximum         building structure using zoning control attributes; this example         shows size in square feet.     -   103 shows the application's ability to calculate a maximum         building structure using zoning control attributes; this         examples shows height in stories.     -   104 shows messages created to give the legalese nature of zoning         and land-use development controls context to the lay person.     -   105 shows the use of policy goals to provide great levels of         understanding of the tone of the zoning controls.     -   106 is the role of the person interested in a land-use         development search such as the buyer, 68, owner, 69, business         owner, 70     -   107 is motivation is linked to the reason for the search     -   108 is experience level and includes knowledge of vocabulary and         terminology     -   109 zoning geo-spatial (GIS) data     -   110 lots and parcels geo-spatial (GIS) data     -   112 boundary geo-spatial (GIS) data such as city limits     -   113 is hierarchy logic for properties with multiple layers of         zoning and/or land-use development controls     -   114 shows base zoning, the primary set of zoning controls     -   115 shows overlay zoning, the secondary set of zoning controls     -   116 is compatibility logic for properties to identify specified         location controls     -   117 is a comprehensive plan, also referred to as a general plan,         that shows long-term planning objectives for a municipality     -   118 refers to future potential based on a future based zoning         control comprising future land use as identified in a         comprehensive plan.     -   119 is future land use code, a description regarding the         projected use classification     -   121 is logic associated with property characteristics as they         relate to zoning and/or land use development controls     -   122 is logic associated with geo-spatial data structuring     -   123 is location change indicator data     -   124 is formulated regulations     -   125 are attributes from property data used to run business logic     -   126 is an abbreviation for a zoning district     -   127 is the name for a zoning district     -   128 is the logic associated with structured changes in zoning     -   129 is part of the process to standardize GIS files in terms of         clipping parcels with city boundaries and reviewing areas within         the city with holes in the zoning GIS data     -   130 is part of the .dbf file that reflects meta data in the         zoning GIS file. These are not standardized across cities but         typically include at least one zoning district identifier that         is used to map GIS data with the zoning ordinance.     -   131 is the overview of the concept behind the zoning complexity         index.     -   132 the descriptor land-use/development focus, also referred to         as code focus, is used to group zoning districts     -   133 is for the baseline or initial time period with number of         parcels by code focus     -   134 is for subsequent time period with corresponding number of         parcels by code focus     -   135 is change in number of parcels between time period by code         focus     -   136 is the change expressed as a percentage     -   137 is the number of zoning districts by code focus for time         period 1     -   138 is the number of zoning districts by code focus for time         period 2     -   139 is the change in the number of zoning districts by code         focus between time periods     -   140 is an overview of the application system     -   141 is a graphical user interface     -   142 is a graphical user interface screen for data input by user     -   143 is plurality of data stores     -   144 is the application     -   145 is the database that stored logic enhanced data     -   146 is the logic or instructions     -   147 is the processor that executes instructions in the         application     -   148 is the standardizing data at the core level using land use         utility classifier list     -   149 is the optional standardization of data at a subcategory         level     -   150 is the results generated using preset options     -   151 is the option to customize     -   152 is a graphical user interface for data output by user     -   153 is an overview of system showing flow for standardizing data     -   154 a graphical user interface for data input and output by user     -   155 is the data module with data stores     -   156 is data store specific to data that does not undergo land         use utility standardized     -   157 processor to execute instructions to standardize plurality         of data sets for land use utility using standardized land use         utility classifier list     -   158 processor for further standardization at subcategory level         and/or hierarchy land use intensity and/or optimized land use         utility determination     -   159 is application where processor, logic and data for         standardized land use utility queries and enhanced logic data         store.     -   160 is the zoning data module     -   161 is the property data module     -   162 is the parcel boundary module     -   163 is geography boundary module and represents one or more         locations     -   164 is flow diagram for determining land use utility     -   165 is an overview of concept for land use intensity hierarchy     -   166 is an output example for a parcel based query with the use         of icons     -   167 shows an optimizing utility finding     -   168 is an option for outputs including map and/or report for         this example     -   169 is an example output typical for a property use record         wherein the same use can be expressed multiple ways in the same         data set.     -   170 is the land use utility classifier list used to standardized         multiple data sets     -   172 is the subcategory land use classifier list reflecting         specialized standardized uses     -   172-173 is the option for showing categories and subcategories         by color and/or icon     -   174 is an example of the various zoned uses wherein the same use         can be expressed multiple ways.     -   175 is an example of the various future land uses wherein the         same use can be expressed multiple ways.     -   176 is a graphical user interface showing location as part of         the query. Location is a requirement     -   177 is a graphical user interface showing options for land use         utility query     -   178 is a graphical user interface showing an option for data         outputs     -   179 is a graphical user interface showing the use of icons as         part of the selection option for lender standards outputs.     -   180 is another graphical user interface and it shows a multitude         of options for breakouts of standardized data.     -   181 is an output of a chart for a city overview by category     -   182 is an output of a chart for multiple cities for one category         “multifamily”     -   183 is an output of a chart for multiple cities for multiple         categories     -   184 is an output of a chart for one geography, a city, with a         detailed breakout of standardized categories by vacant and         improved     -   185 is a view of an output, an electronic map. Note: spatial         data is required to create map tiles or layers and, in this         example, 186, shows tile color based on an opportunity for         multifamily using standardized data.     -   187 is shows a graphical user interface for Hierarchy Intensity         wherein the default is as shown in FIG. 54 while this gives user         opportunity to reorganize and/or add data elements from property         data record as shown in FIG. 62.     -   188 is a graphical user interface showing search and the option         to save preferences or start new as well as to search by         subcategory, specialized uses shown in 189 with icons.     -   189 is a summary overview of the invention for standardized land         use utility and the inherent nature of property use by time.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The following detailed description is directed to certain specific embodiments of the invention. However, the invention can be embodied in a multitude of different ways as defined and covered by the claims and their equivalents. In this description, reference is made to the drawings wherein like parts are designated with like numerals throughout.

Unless otherwise noted in this specification or in the claims, all of the terms used in the specification and the claims will have the meanings normally ascribed to these terms by workers in the art.

Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising” and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in a sense of “including, but not limited to.” Words using the singular or plural number also include the plural or singular number, respectively. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, shall refer to this application as a whole and not to any particular portions of this application.

Referring to FIG. 1, a fly out box is shown and shows a reference to another code section. As various municipal code sections are referenced within other code sections, one feature of the invention is the inclusion of all referenced code sections in an analysis of proposed uses for a given property with a given zoning value.

Referring to FIG. 2a , a table is presented wherein property uses are delineated for individual look ups by a particular code or use and its approval rating by the code. For example, Principal Uses (permitted by right) and Conditional Uses (subject to commission or special approval) are presented to the right of three listed zoning districts.

Referring to FIG. 2b , a table of translations is presented. Terms of zoning code “use” information is presented next to lay categories that are understandable to lay people. The presented segregation of data allows for a reverse-directory search for data.

FIG. 3 presents a disclosed method of delineating Permitted Use and Conditional Use by zoning codes and specified uses such as Single Family Home. The “P” shows Permitted Uses and “C” shows Conditional Uses. The rows show specified uses and the columns show zoning codes. A unique data retrieval methodology of an embodiment of the invention is the entering of individual uses into a database associated with multiple zoning codes.

FIG. 4 presents rows for zoning districts and columns for some of the size criteria used in zoning: lot size, floor area ratio and setbacks.

FIG. 5 presents one method of delineating zoning codes and lot size data upon a spread sheet that is entered into a database. Not only is the “lot size” data used but so are the other requirements such as “maximum height” for the building(s).

FIG. 6 presents a sample generated report using a disclosed method to a) show a comprehensive overview of the zoning code's basics (uses and size) and b) to check these requirements for a particular property. The goal is to check for uniformity. This specific property's data can be auto-filled from a third-party data provider such as public record data or the user can enter the data to show the property “as is” or to test various scenarios.

FIG. 7 presents a pseudo screen shot with a disclosed interface in facilitating a search of the disclosed data base, such a search might include a zoning district, a specific property, a use (excludes parking/signage/“watch use” and “watch code.”) The interface supports multiple views and pivot points to examine zoning code data. In various embodiments of the invention, a user, such as a non land-use expert, may focus upon on data retrieval as well as manipulation to add organization and greater comparability and comprehension of zoning codes and other building restraints.

FIG. 8 presents a pseudo screen shot of a provided process and interface comparing size data, specified zoning codes, for a specified city or specified county.

FIG. 9 presents a pseudo screen shot showing process of looking up “Use” data by a specified zoning code for a specified city or specified county.

FIG. 10 presents a pseudo screen shot of a disclosed process of looking up “Specified Use” and the results show what zoning codes allow that specified use. Additionally, the users could search by a brand name to find out what zoning codes support that use. This is geared primarily to help a business find a potentially suitable location. The system may also be used to communicate with the municipal planner to describe with “use” they are applying for.

FIG. 11 presents a Zonability Flexibility Rating chart. In one embodiment, the Zonability Flexibility Rating chart rates a specific zoning code en masse, not by a specific property. Elements that may be used to derive a Zonability Flexibility Rating include: prominence of zoning code in particular market, number of uses permitted by a particular zoning code, number of uses requiring a conditional use permit, size related rules, exclusions and exemptions, parking requirements, design review requirements, rebuilding options in case of destruction, bonus densities, energy efficiency requirements, and moratorium uses or encouraged uses for land.

FIG. 12 presents an explanation for one type of Zoning Score which reflects a specific property. FIG. 12 also presents one set of steps used to derive a Zoning Score. The Zoning Score may be auto-generated using available data. The Zoning Score can change as the user interacts with the data.

FIG. 13 presents a sample of CC&R restrictions sorted by categories. Such CC&R may be used to define uses that are prohibited or permitted. Such permitted and unpermitted uses may be collected by one embodiment of the disclosed invention and used for ascertaining permitted uses or the desirability of acquiring a property.

FIG. 14 presents a pseudo screen shot of how one embodiment of the disclosed invention could be used in a typical third party website as a widget or API (application program interface).

FIG. 15 presents a “size” related output for a selected zoning code. This table may be the product of an ancillary “size” widget that uses or works with one embodiment of the invention.

FIG. 16 presents a “use” related output for a selected zoning code. This table may be the product of an ancillary “use” widget that uses or works with one embodiment of the invention.

FIG. 17 presents a table of calculations derived from a data provider like GIS technology to target a specific parcel's data including it zoning code(s) and other attributes such as lot size, house/building size and other attributes.

FIG. 18 presents a pseudo screenshot using GIS technology wherein a user has been prompted to enter an address and wherein a user has received a “details” hyperlink to view further information regarding the entered address. Such details are shown in FIG. 19a , and include, lot number, land use, lot area and name of neighborhood.

FIG. 19b presents further “details” from the hyperlink of FIG. 18. Such additional details include content from a screenshot using GIS technology. The available “details” provide extensive content culled from a database such as GIS. database. Such details include allowed uses and other data elements. The GIS provides layers used to create unique interactive maps with zoning ordinance data and related data embedded. GIS maps manipulated with database data show percentages by use (residential, commercial, mixed, industrial public) by block, specified neighborhood or radius from a specified location.

FIG. 20a presents a pseudo screenshot using GIS technology and shows a sample of data culled from the database and the calculations using the development standard rules. The figure presents one embodiment of Z.O.E. also known as a “zoning ordinance evaluator” tool.

FIG. 20b presents another pseudo screenshot using GIS technology and shows another Z.O.E.

FIG. 20c presents yet another pseudo screenshot using GIS technology with a Z.O.E. showing the compliance or non-compliance with various zoning restrictions.

FIG. 20d presents yet another pseudo screenshot using GIS technology with a Z.O.E. showing the compliance or non-compliance with various zoning restrictions and showing lot and zoning information.

FIG. 21 presents a pseudo screenshot of one contemplated embodiment of the invention. This embodiment includes an interactive map that allows users to click upon either a map label for zoning information or to click upon a parcel to obtain further information such as zoning, code name, description or rules. These maps can be generated by neighborhood, block or a radius from a specified location.

FIG. 22 presents a pseudo screenshot of one contemplated embodiment of the invention, featuring Zoning Details a function with the ability to organize data by zoning code including peripheral data relevant to understanding the impact of zoning such as the number of properties zoned, the average lot size and building size (in comparison to the zoning code rules), the variances, planning commissioner meeting notes and other data elements. This data can be made mobile through the creation of a widget or API.

FIG. 23 presents a pseudo screenshot of one embodiment of the invention use to evaluate condominiums, one example of a use that has Covenants, Conditions and Restrictions.

FIG. 24 presents an example of a pre-formatted letter allowing a city or county planner to select answers to save time. The invention incudes embodiments wherein reports are automatically generated by use of the disclosed system.

Referring to FIG. 25 shows a simplistic view of the invention allowing for self-service, computer implemented search, 1, through an application that consists of several parts. The structured database, 4, combines spatial data, zoning controls in the form of structured data, environmental data, transportation, neighborhood, property data, historic and economic. In the element for logic, 5, the rules relevant to the subject of zoning and land-use development controls comprise compatibility, calculations, property characteristics and spatial data are handled. Answers, 3, return results in several formats comprising reports, score, application programming interface, maps, data and inquiry

Referring to FIG. 26A, the consumer, 21, has a manual and multi-step process to access, 22, zoning and land-use development controls by phoning, emailing or visiting in person a government agency, 23, for information, 46. There are two types of zoning information a consumer receives: 1. Unofficial, 49, which is obtained from an agency free of charge and is casually dispensed; and 2) official, 48, information for which the consumer is required to submit for the request, often times for a monetary fee, to get back what is often times called a letter of verification or letter of determination within a time frame spanning multiple business days that explains the zoning controls on the property. In the case of a letter of determination, it expands the scope of work to return an initial planner's view of the proposed use and/or development for a specific property. The limited online tools provided by the government agency, 23, are often too complicated for the layperson to know how to use primarily because the online tools focus on an agency and/or jurisdiction control. For instance, the property data, from a tax assessor, is a different agency and often times jurisdiction (county vs. city) yet it is the zoning controls paired with property data that provides for answers relevant to a consumer. A consumer may contact an expert, 24, to get their questions answered. The experts will have the experience to navigate the on line tools provided by the government agency and/or may opt to call the agency for information. The consumer will likely hire and pay the expert, allowing time for the expert to gather, analyze and respond. The key differences between the invention and either the government planner or private planner is time and/or money and/or level of detail. While the private planner may provide a detailed report for their customers, the government planner's outputs lack contetxtual details and tend to be useful to users with expert experience to read and comprehend the outputs.

Referring to FIG. 26B, it is a visual expression of the decision tree for assembling zoning information with a flow regarding the process for zoning referred to as zoning action, 50. The invention automatically gathers the information, 46, to first understanding the zoning controls, 7, relative to a specific property or properties, 11, looking for compliance, 47, “yes” or “no” with an official, 48, or unofficial, 49, check of the property's compliance. The perspective, 31 includes roles of parties motivated to get the information and comprise owner, seller, agent. The zoning action, 50, is the process of submitting officially to gain permission for some type of action such as rezoning or requesting a variance. The invention is designed to provide answers for the consumer in their weighing of decision to undergo a zoning process. Whether an government agency or expert, the steps require the person(s) understand the rules associated with a property or properties, compare the rules to the property or properties and match findings to motivation of the consumer to return answers about the need to undergo a zoning process and the likelihood of achieving a zoning permit.

FIG. 27 illustrates the application with three identified groups to use the system including the consumer, 21, the experts, 24, and the government agencies, 23. The structured database, 4, and logic, 5, store data and support the execution and retrieval of queries. The inventions provides an option for government agencies, 23, to use the system across multiple agencies with a singular platform offering continuity and a method to automate tasks, 28, which comprise letters of verification and/or letters of determination, character area analyses and planning capacity study reports. The invention allows for the consumer, 21, to work with experts, 24, in the form of preformatted inquiries, 27. The experts can work from the same system, 29, to work more efficiently with government agencies on consumer directed inquiries.

FIG. 28A provides an overview of the application to show the flow from the initial search, 30, to getting answers in various formats, 33. The user is given the opportunity to add perspective, 31, which includes the role, experience level and motivation for wanting to get their inquiry or inquiries answered. The application, 2, retrieves structured data, 40 (spatial) and 41 (non spatial), in the database, 4, with logic, 5, as needed for the query. A data record, 32, is procured and the option to iterate, 42, by editing the query, and/or to filter, 43, by sifting through the retrieved data records, is offered. The answers, 3, can then be returned in various format, 33.

FIG. 28B provides details for search, 30, comprising property, 35, use, 36, hypothetical, 37, attribute, 38, and/or electronic map, 39 and answers, 3, comprising data, score, report, api, electronic map, api and inquiries with the option to edit, 44, the report format. The property search, 35, is used when the location is known but the zoning controls relative to the property or properties is unknown. The use search, 36, is used when the use is known but the location or locations is unknown. The hypothetical search, 37, is used when a property or group of properties is known but testing of scenarios is desired to see “what if” possible outcomes. This search enables the user test zoning actions comprising rezoning, changing a use permission, changing a development standard and selecting one ore more variables to test the hypotheticals. Through this process, the user is able to gain a sense of the power of zoning controls by automatically seeing outcomes. It is also applicable to creating automated capacity studies in which variables are tested automatically generating results. Search by attribute, 38, allows for a query based on the notion the user knows the attribute but not the data associated with it. Search by electronic map, 39, is a search initiated on an electronic map allowing for geo-location functionality in addition to the search by property, 35, use, 36, hypothetical, 37, and/or attribute 38. The combination search, 34, allows the search to use one or more of the search. For example, a user could search for a “use” such as medical office and “property” to specify a location. The structured database, 4, stores structured spatial data, 40, and non-spatial data, 41. The invention uses spatial data which allows for the association of data on a latitude and longitude basis. While the current format is known as geographical information system (GIS), the invention is able to adapt. The logic, 5, allows for the application to automatically calculate development potential for a property or properties and provide compatibility analysis to identify situations that don't meet requirements for distance-to for protected uses such as churches and schools, adjacency zoning issues such as a commercial district adjacent to residential and/or street type controlling uses to protect or promote identifiable developments and/or uses.

FIG. 28C provides potential search options. The invention allows for robust search across multiple jurisdictions due to the structuring of data with logic. The invention solves the problem of focusing on a specific location and then identifying the land-use development potential and/or use restrictions. Instead, the user can query data to find results based on their specific interest in knowing the land-use development potential and/or use restrictions as well as the contextual data around the subject such as “location change indicators”, 67, which provides data analytics regarding physical changes completed, underway or planned in the queried area. Another example is to run a search for properties allowing a specific use, 56, in which the structured data base is able to return parcel-level data for matches.

Referring to FIG. 28D, the perspective allows for a more personalized search experience because the role, 106 and motivation, 107 influence the level of data retrieval required comprising calculations, comparisons and messages. Adding a level of zoning and entitlement experience in a specific jurisdiction, 108, further articulates the user's perspective. Examples of perspective, are: 1) an owner/user of a one-story commercial building with no prior experience with zoning is interested in knowing if it looks possible to redevelop it into a multi-story building in the future. The zoning controls for building size and likelihood of obtaining permission are at the root of the question. Because the user is not experienced, definitions will include beginner level guidance notes with the data response. 2) a potential investor is interested in buying the same one-story commercial building and is well experienced in zoning matters and, in particular, for that municipality. The zoning controls for building size and likelihood of obtaining permissions are at the root of the question. Because the user is experienced, no definitions are returned with the data response.

FIG. 29 focuses on the spatial data used in the application. While the structuring of the zoning ordinance and land-use development rules create a self-contained search and retrieval with organization of the data, the spatial data creates an association of that data with a location with varying levels of granularity. The spatial data for zoning, 109, creates polygons for geo-spatial locations but this level can't specify the levels of regulation at a defined parcel. This is why spatial data for lots and parcels, 110, is incorporated. The spatial data for boundaries, 111, allows for blocks of search by a geo-spatial defined area or areas such as zip code, city boundary and/or neighborhood boundaries.

FIG. 30A provides greater detail as to the types of data spatial, 40, nonspatial, 41, and the logic, 5, used in the invention. Spatial data, 40, forms the association of a property's location with land-use controls which are often set up geographically. The non-spatial data, 41, provides contextual data regarding the land-use controls and the property. The logic, 5, handles hierarchy and compatibility type of logic using rules associated with property characteristics, 121, and GIS spatial data structuring, 122. Hierarchy, 113, has to do with the situation arising from multiple layers of regulations on a parcel and knowing which layer is valid for which zoning control and is further explained on FIG. 30D. The compatibility logic, 116, has to do with the nature of zoning and land-use development in terms of how permissions and limits are expressed and is further explained on FIG. 30B.

FIG. 30B shows the logic for testing compatibility, 116, in which a rule or series of rules specify location of uses by stipulating a distance-to requirement and/or adjacent zoning and/or adjacent existing uses and/or a combination.

FIG. 30C refers to creating a test for compatibility issues by selecting a location and retrieving potential compatibility conflicts. The exhibit shows an “x” which is meant to represent a protected use, such as a school, that can impact development potential around it. Item “1” is meant to show such a location that may be zoned to allow for a “bar” but due to the proximity to the protected use, it is not a suitable candidate for that use. Item “2” shows as having no compatibility issue. Item “3” shows an issue with the “x” being another known protected use, residential.

FIG. 30D provides an example of a property with multiple layers of regulations requiring the use of logic for hierarchy, 113, to enable the return of data applicable to the requested search. The base zoning, 114, has primary control, and reflects the item labeled “MF-4” and identified with a circled number “1”. The overlay zoning, 115, has additional policies to model and is identified by the three labeled items “UNO”, “RDS” and “NP” with a circled number “2”.

FIG. 31 is a return of a data output with the electronic map displaying capabilities of associating a zoning control and/or descriptor with a location or locations automatically. A descriptor is a way to describe a zoning policy that is either trying to encourage or discourage development and/or change. The map legend is color coded to associate a location with a zoning control and/or descriptor.

FIG. 32 shows a that descriptor is subjective but it can be based on objective data given the structured database with zoning controls, zoning permits etc. For example, the descriptor “design” has to do with requirements for attributes comprising glass, paint color, architectural details, parking placement. The descriptors can be associated with other information such as definitions, zoning cases and/or news stories involving zoning.

FIG. 33 is a screenshot of the input for the zoning controls associated with development standards. Each zoning ordinance has its own way to express building size and form as well as placement, design and additional structures. The application is able to coalesce the data required to return a uniform output for estimated maximum building size, lot size requirements, heights etc. for multiple jurisdictions.

FIG. 34 shows a screenshot of the structured data creating a dashboard from which zoning controls can be viewed, edited and compared. The organization of data with geo-spatial data allows for the user to know the inventory of a jurisdiction, neighborhood or other boundary by querying the data for the properties allowing a specified use and/or that meet a lot size and/or building size criteria and/or year built. For example, demand for data centers could be the focus of a query in which the user asks which districts permit the use and then have all parcels return with an supply report comprising number of parcels, size of parcels and locations. When paired with property data, the query for supply can expand to include existing uses, ownership, current improvements etc.

Referring to FIG. 35A, the invention's structuring of data creates an opportunity to use it for compatible applications such as building information modeling, a technology being used by engineering companies to help the community visual the proposed projects. This is of great use in building community support for a planned and/or proposed project, especially infrastructure and/or private-public projects impacting the real estate located adjacent and nearby.

FIG. 35B shows the use of location change indicator data, 123, in which the economic drivers for new construction are made readily available.

FIG. 36 shows a hypothetical using a search based on economic development corporation sponsored projects since such investments can work to change the area around the investment. The user identifies the property or properties and sets up the hypothetical zoning action, in this example “rezoning”, to query what zoning district or districts allow for a building height of at least 3 stories and then to apply those rules to the identified property or properties. Other queries are possible regarding the approved economic development project and/or the brand. The invention allows for branded development. Most, if not all, economic development corporation sponsored projects involve a brand. The collection and structure of the data can reflect development standards associated with the planned project, what is typical for the brand, typical lot size requirements etc. and pair the economics of the approved or branded project such as store sales, per square foot, earnings, number of employees etc.

FIG. 37 is a view of search by use where the user knows one or more uses and wants to know where they can be located. This can be accomplished for one or more jurisdictions using a methodology, 81, of standardizing uses from a zoning ordinance to other classification systems to allow for search by use.

FIG. 38 shows another view of search by use in which the user is also able to query the database for proposed change, 83, options and business operations, 84. Zoning controls hours of operating, outdoor display, outdoor seating, drive through windows, walk up take out windows and size related data such as number of employees, tables, etc. The application is able to use structured data and logic to return applicable results.

FIG. 39A is another way to search by image and brand. The image search allows the user to send a query from a mobile device, 85. The user's geo location is identified and results returned. FIG. 39B search by brand, 86, allows the user to select an image and/or logo and query where the branded development might be allowed, or not. The data associated with a branded type of development is added to the database.

FIG. 40A refers to output from a search in which multiple locations are possible with search by use, 36. The query, 94, allows the user to articulate the type of search with the option to have the query focus on data preferred by the user, 93. The results, 87 and FIG. 40B, provide multiple ways for the user to interact with the data. The user can click on a map icon to get parcel level information with or without neighboring properties. The map legend, 89, express zoning controls by coloring the electronic map. The user can opt to change these as part of a hypothetical search, 37, which can be combined as part of another search, 30.

FIG. 40B shows the number of returns, 95, a description of each return, 97, in a list, 96, with functionality

FIG. 40C shows an example of output by use permissions with a side-by-side comparison, 101, for multiple properties. The “i” is useful to providing additional help in guiding the user. The permissions include likely ok, 98, maybe ok, 99, and unlikely, 100, which correspond to permitted, conditional and prohibited. The application is able to highlight uses based on the user's query. Use permissions associated with physical property requirements such as a lot size can be factored in through the logic, 121. For instance, if the rule requires the lot be at least “x” size, the application can take that into consideration and retrieve relevant returns.

FIG. 40D shows an example of an output for a maximum building potential using calculations, data retrieval and logic. The application can convert the returns to metric, stories to feet and feet to stories, square feet to acres and acres to square feet etc. The development potential estimate uses logic which formulates a hierarchy to know to return results based on the trumping effect between multiple layers of zoning and/or land-use development controls. For instance, the rule for height could be retained by the primary base zoning but the land-use development control for watersheds, for example, could constraint the maximum building size. The ability to connect numeric outputs to relevancy for the area and/or relative-to comparison as well as connecting location change indicator data such as demographics to results.

FIG. 40E shows an example of the output with messages created to provide greater understanding of the district, development standard and/or use and/or other zoning control. The policy goal is used to help provide greater context to the user and can also be used to improve logic associated with search and data retrieval. For instance, a auto-related use may be conditional meaning some form of special permission is needed to ensure the municipality has had the chance to review the situation prior to providing a permit. If the policy goals are slanted towards “environmental” or “pedestrian friendly” the policy's tone could be anti-auto. The data return could indicate the probability for the use being approved at that location is lower than in another area without the same policy.

FIG. 40F refers to the report output with property data, 125, used to run calculations, comparisons, lists potential restrictions and check for jurisidiction controls over land-use and zoning.

FIG. 40G shows the visual representation of the calculations performed on a select property with deltas to show “relative to” the existing conditions and potential.

FIG. 40H the data output can include data exportable to use in a spreadsheet; the output also includes an API, application programming interface, to allow for the data to be used in other applications

FIG. 41 shows a hypothetical. The user selects an area and asks to apply a different lot size requirement for the use “duplex”. This provides a planning tool as well as a way for a user to understand the implications of zoning controls. It can be used to find suitable sites for development, possibly requiring a zoning action to obtain the zoning permits. When it comes to development, entitlements are part of the soft costs along with fees and holding costs. The hard costs are the construction materials and labor. A user making a decision to go forward with a zoning action is interested in knowing the outcomes from similar cases. This is why the invention includes permit data to allow for the user to weigh their options.

FIG. 42 refers to future planning determinations, 118, as per a comprehensive plan, 117, which create future land use codes, 119. The future plans create future land use codes which are descriptive but without the zoning controls. Examples include: “high density residential”, “office”, “mixed-use.” Spatial data contains the variables for a comprehensive plan in draft and/or finalize format. The comprehensive plan is sometimes called a general plan and looks out 10 or more years as to the planning needs for a municipality. Once approved, a comprehensive plans guides decisions made using future land use codes as an indicator of what development a municipality wants at a specified location. It works with zoning controls because in instances where the zoning and the future land use code vary, if a user is motivated to change a property, the municipality has the power to not allow the change given the comprehensive plan which is land-use development control. In cases in which a property is currently zoned for industrial use and has a future land use code of mixed-use, a rezoning request to change the zoning to support mixed-use development is allowed because it adheres to the municipality's vision. This invention's inclusion of permit data allows for the search and retrieval of data points relative to the zoning process and relative to the existing zoning as well as approved rezoning. Data regarding the subject of rezoning approval or disapproval and/or another zoning action such as variances, conditional use permits issued in the past may help in creating a pattern for the potential for future approvals.

FIG. 43 shows a outcome of using spatial data from multiple governmental agencies in a structured database issues about data anomalies come up and some are mistakes while others are reflect the realities. The parcel in the example spans across two county boundaries and appears to be reflect a reality. The application is able to handle spatial data structuring, 122.

FIG. 44 is an example of applying the application to water districts which are similar to zoning districts in that they have names, abbreviations and associations to plans and/or ordinances with controls on property development and use potential (its utility). The future for zoning could intertwine the use of water to a greater degree than is currently expressed. For instance, a jurisdiction could require water, and other utilities, to have a measurement base level associated with a use and/or building type and build regulation around meeting the requirement, similar to how traffic impact analysis (TIA) studies are used currently. A use may be allowed but “subject to” a TIA with a pre-set constraint. Should the TIA show excess traffic, the zoning permit may be denied. The land-use development codes may include water-related constraints such as watersheds. While watersheds have development controls that function like a zoning control and impact a property's utility, the linkage to water districts is not opaque. This invention makes the connection of water scarcity and/or availability relative to a location thus making water rights more transparent. Furthermore, as a way to disclose additional information, the hypothetical.

FIG. 45 shows the invention's ability to retrieve data based on the user's perspective as the issue has been the “one size fits all” mentality of planning departments to dispense information. The invention's structured database and logic allows for data returns to be organized to meet the level of the user's experience with the local zoning and real estate development, their role and motivation.

FIG. 46 shows the invention's data structure applied to formulated regulations, 124, such as transit oriented developments (TOD) and transect zones in which preconceived controls are used in multiple jurisdictions with the option to calibrate or alter the controls to meet the specific jurisdiction's needs. The common themes are captured by the application including the controls allowing for their implementation in hypothetical scenarios in addition to its incorporation as a zoning and/or land use development code regulating a property's development potential and use. The invention is able to segment data regardless of the type, meaning the new urbanism can be modeled as well as the conventional.

FIG. 47 shows the unique articulation of zoning change at that parcel level with descriptors to show the “who,” “why” and “what” for zoning change. This requires a combination of automated data processing and some manual parsing of publicly available documents to connect the reason for the zoning change and whether it was the owner, city or another party who was responsible for initiating it. Through the use of standardized descriptors around zoning change, it is possible to see the history of a city's zoning through helpful associations such as date and the reason for the change in zoning.

FIG. 48 shows a GIS zoning file and is updated periodically by the city controlling the zoning; however, the GIS zoning file does not articulate changes in zoning. This is where it is needed to have a baseline time period for a city in which its GIS zoning has been mapped to its ordinance and the parcels associated with the city are able to create an inventory of the number of parcels zoned by a descriptor from one time period to another.

FIG. 49 shows output from GIS zoning file with identification of zoning. This is typically what is found in a zoning GIS file. It does not identify just those areas with zoning change. Furthermore, there are no descriptors which is why this invention includes an association of the GIS mapping data with the zoning ordinance and allows for the consistent use of descriptors. By consistently gathering, processing and standardizing the data by time period, and combining with parcels, it is possible to chart the changes in a city over time with greater levels of insights into zoning changes for one or cities.

FIG. 50 shows output from two different time periods for the same jurisdiction with a breakdown of the number of parcels by a zoning descriptor noted as part of this invention specific to land-use and development and noted as the zoning code focus. In this figure, the table shows it as focus name. It is the through gathering of zoning data over multiple periods and handling the data in a consistent manner that creates outputs of useful data for a zoning change index.

FIG. 51 illustrates an overview of the system including the use of an input interface for receiving information, the need for data storage, logic and processing of instructions and an output 140. There is a user interface 141, a graphical user interface, with options for data input screens 142 and data output screen 152. The data input imports data from a plurality of data sources 143 which include a date representing data date to allow for time series and analysis. These data sets are processed in the application 144 that includes a database 145 that can store spatial data and nonspatial data while the logic 146 includes instructions for parsing data files, intersecting parcel boundaries with zoning and/future land use GIS data, standardizing for land use utility, associating property record data with standardized data and creating query returns using stored logic and delivering outputs 150 and 152. The application uses a processor 147 to execute instructions for the different functions including in the standardizing data module 148 which offers the option to further refine at a subcategory classifier level 149. Once data is standardized, it is stored in the database 145 and used with data that does not need to be processed for standardized land use utility. Other boundary data representing location may be stored. The data outputs 152 display data results by pre-set options 150 or allows for customization 151 and are accessed via a graphical user interface 141.

FIG. 52A is a flow diagram of an overview highlighting the modules within the application 153 and how the combination of an interface 154, data store 155, standardization process 157, processors executing computer instruction including for creating hierarchy and specialized subcategories 158 using data 156, work together to create one or more specialized databases for use within or by the system and a method for standardizing land use utility 159. The data 156 includes spatial data used for data module zoning and parcels while property data is a tabular or nonspatial data form which data store handles 155. The processor executing instructions for standardizing 157 execute a number of functions in order to take multiple data sets and standardizing “use” to a common land use utility classifier list. The standardization process can benefit from machine learning whereby patterns that repeat and the use of “truth data” can be used to better improve the results. Furthermore, the disclosed invention could benefit from expanding to artificial intelligence once more data and logic is written to build instructions to train the system what to look for in the myriad of data stores beyond what is currently being claimed. These could include GIS files that have meta data related to newly issued zoning permits that could, for instance, be used for improving the quality of the optimal standardized land use utility of utility patterns index.

FIG. 52B shows key modules for the application. The zoning module 160 uses spatial data and that is intersected with parcel boundaries 162 and through intersection is able to convey multiple zoning districts (and/or future land use designators) associated with a single parcel, reflecting a “one to many” relationship. A zoning district can have one or more zoned uses associated with it that convey land use utility. These uses are mapped to a common land use utility classifier list. Property data 161 is in a tabular format and contains multiple fields and is parsed for the field or fields referring to current or existing use. These are mapped to a common land use utility classifier list. Other fields in the data such as owner name, building size etc. are stored for use in the application. Parcel boundaries are a spatial data format 162 that contain geometries and an id that can be associated with one or more property records. A parcel boundary is associated with one or more zoning districts through an intersection process. Geography boundaries define one or more locations 163 and are used to associate one or more parcels and can be used a value for comparing and tracking standardized land utility data.

FIG. 53 is a diagram outlining what instructions need to be used for determining land use utility 164 using standardized data and a series of IF/THEN statements. IF standardized current property use and current zoning are the same, THEN the standardized land use category is returned. IF standardized current property use and current zoning are not the same, THEN the standardized land use category by data source is returned and parcel is identified as an “opportunity” IF standardized zoning category is more intense than standardize current use. Next, if a check of future land use (FLU) is being done, another set of IF/THEN statements are applied and IF the standardized zoning category is the same as FLU category, THEN the standardized land use category is identified and supports “optimal” land use utility. IF the standardized zoning category is not the same as FLU, THEN the standardized land use categories are returned with data source identified and parcel is identified as an “opportunity” IF standardized FLU is more intense than standardize zoning category.

F returns with an option to show source and/or option to check standardized future land use. If the current use is “vacant” there is no standardized land use category reflecting status in zoning and/or future land use data. The logic tags “vacant” status and IF standardized future land use is checked for parcel, THEN future land use will be returned as the optimal use. As FIG. 54 shows regarding hierarchy of land use category, there is also a hierarchy for time based on property being vacant or not. When data returns for a parcel vary by current property use, zoning, future land use—all standardized—the return will include source to acknowledge which data is showing which standardization.

FIG. 54 shows land intensity categories that create a hierarchy 165 and can be used to express a higher and better use relative to another category. Agricultural is the least intensive while Industrial is the most intense. The hierarchy is relative to the land use category. When it comes to an Optimal Land Use Utility, consideration may include neighboring properties, vacant land status and lot size.

FIG. 55 show an output at the parcel level and includes a comparison between subject and three additional parcels. Each parcel shows a data returns using an icon with the key referencing the category and includes a “yes” or “no” for opportunity and indicates an optimal land use utility finding 167 using the concept of hierarchy and presenting an Optimal Standardized Land Use Confidence return with the intention of making the likelihood of achieving optimal land use utility based on the location including any specific parcel related “relate to” comparison as shown in this example where a “subject” property is identified.

FIG. 56A-56C show the three data sources being standardized with the left-hand box 169 showing typical outputs from associated data sources and how a use is mapped to the Land Use Utility Classifier list on the right-hand side 170. A use can also be further mapped to a subcategory 171 to create a specialized utility classifier 173. By doing so, it creates additional continuity for the data and enhances search, analysis and assessment. The option denote color and/or icon by category and/or subcategory is also shown 172. Each individual FIG. 56A-56C is also explained in detail.

56A shows property record data and the fields for current use are standardized and this can include a status of “vacant.” One of the categories for Land Use Utility Classifier is “vacant” and because a property denoted as “vacant” may also have a descriptor for a category such as “residential land,” the current use can be mapped to the classifier list as “residential” and “vacant.” While the property record may show “residential” this does not mean the zoning and/or future land use will correlate since public record has a flaw in that it is at the county jurisdiction level while zoning is handed at the municipal jurisdiction level.

FIG. 56B shows an output for zoning uses. As described, the zoning districts are intersected with the parcel data which is associated with the property record. A zoning district indicates what uses are allowed by that zoning district and are expressed as “zoned for use” in FIG. 56B. Since no two zoning ordinances are alike and zoned uses are not standardized between different zoning ordinances, the benefit to mapping to a classifier and subcategory classifier list is displayed in this exhibit and has been described by this invention in the past. The key difference is that before also standardizing the current use, the invention was limited since so many people have a difficult time grasping how zoning, an intangible, relates to a current use, often a tangible, i.e., a house, office building etc.

56C provides an overview of future land use and like the other two, the uses are mapped to the classifier list and/or subcategory classifier list. Like zoned uses, each municipality expresses future use differently. Some municipalities use very specific terms like “apartments” while others may opt to use more conceptual terms like “town center.”

FIG. 57A is a graphical user interface (GUI) and shows location inputs 176, which are a requirement and the ability to use “saved” preferences or start new and opt to save, including being able to save a number of query options 177. If a user only selects an address, the output will reflect all standardized land use utility categories for the location as a default and the user can opt to filter. The purpose of the GUI is to allow user to make a targeted query and user can opt to save. Output options include being able to get returns using lender standards with the example showing the use of icons to represent the type of search. Lending standards for residential loans are for residential properties with one to four (1-4) dwelling units while a commercial loan is for multifamily with five or more units (5+) and nonresidential uses. The issue is a zoning ordinance often cites three or more (3+) units. This means a property may have four units and be zoned for “multifamily” but have “residential” lending standard status. The need to be able to identify one to four (1-4) residentially zoned properties is benefit of this invention as it can be useful not only for search but also tracking changes as more dwelling units may be allowed by zoning through the use of accessory dwelling unit initiatives for instance. The standardized data at both the core and subcategory level are useful for this type of analysis and land use utility understanding. The invention allows for search by different types of standardized land use utility at different geographic levels—at a parcel, city or metro etc. 176. The invention also allows user to draw an area to create a boundary to use as a sub-location.

FIG. 57B is another view of a graphical user interface whereby the user has the option to include land use utility categories and/or get a breakout of the data by one of several options 180. These options are due to the standardization of multiple data sources with a data date and the further refinements to the classifier list and/or to the land use determination to find opportunities using standardization and hierarchy of land use utility disclosed.

FIG. 58 shows an output of chart visualization of the data for a city by multiple categories 181 with outputs for current and future. Current includes standardized current use and current zoning while future includes standardized future land use. The view can be changed to reflect “improved” vs. “vacant” and other breakouts 180.

FIG. 59A shows a metro-to-metro comparison using an output of chart visualization of the data reflecting a category, multifamily 182. The ability to land supply is a key part of this invention and by different metrics and breakouts 180. The land area can be reflected by number of parcels and/or land area in acres (convertible to metric system).

FIG. 59B shows a combination of different land use categories for multiple metros 183 and is useful for tracking and forecasting using standardized data for land supply with breakouts to show improved vs. vacant etc. 180.

FIG. 59C shows a breakout for a single city with detailed breakouts for land use utility 184 which can be applied in any of the chart or data outputs. The ability to filter results 180 and apply other data sets useful to land supply analysis such as census, age of buildings, ratio of existing building to lot size and to be able to make comparisons to other locations and track changes as well as view data in different visualizations, automate finding opportunities and refining search shows the flexibility and extendibility of this invention.

FIG. 60 shows an output of data in an electronic map 185 and while this is one of the data outputs, others related to maps and geometries include map layers and/or vector tiles and/or formats that are able to represent simple geographical features and their non-spatial attributes like geoJSON. This visual also shows transforming standardized land use utility to make a map show the story the user is trying to tell whether that is visually identifying only those locations where there is an opportunity by land use category 186 or to block out noise such as being able to visually show vacant (or improved etc.). status for a location. Again, these are metrics that can be tracked over time due to standardization.

FIG. 61 shows a visualization of the data in a dashboard format 187. The breakouts 180 give a sense of how the data can be filtered and viewed. Data can be exported in a visual representation, report or by geometries like vector tiles.

FIG. 62 is a graphical user interface showing search using land use utility standardization 188 and helps explain how standardizing changes real estate search and analysis. The subcategories 189, can further refine search to returns data meeting the criteria. For instance, a user conducting a search by standardized land use utility, using category “residential” for current property use and adding “2-4 units” as a subcategory option 189 and “include vacant” and “use same” for standardized zoning use, will search for those parcels identified as having a current property use of 2-4 units or vacant land and identified by standardized zoning for potential residential with 2-4 units for search area Atlanta, Ga. MSA. The results returned will refine the returns to those parcels meeting the criteria described and with a standardized future land use utility classification of “mixed use.” This search could be further refined by other property data such as lot size range etc. and reflect a time period such as “how many parcels met these conditions as of ‘x’ date?” and “how does that compare to now?”

FIG. 63 is a summary of the invention and the idea that by standardizing multiple data sets used in the real estate industry to a common classifier list that regardless of the way an assessor reflects a current property use in a property record or how a zoning district expresses a use for a zoning district or how a future land use plan reflects a use for the future, the terminology for a “use” is standardized and the geography established by a parcel location relative to one or more geographical boundaries. In short, the standardization process creates a new way to track, map, search and analyze location and property across multiple geographies 189.

Multiple Utility of the Various Embodiments of the Invention

Zoning is the “open sesame” of real estate and yet the existing methodologies to obtain such pertinent data are buried in cumbersome zoning ordinances or similar property related such as covenants, conditions and restrictions. This invention removes the need to go through this time consuming process and allows the user to look up specific information requested as it relates to size, use, parking, signage, code summary and a specific property's information as well as various related municipal ordinances such as “green ordinances”.

Data once retrieved can be exported into a printed report format or into a .cvs format. Methods, developed as a result of the research conducted in analyzing zoning code data, provide the basis for a Zoning Flexibility Rating, which reflects the rating for a zoning code based on a variety of variables. The Zoning Score in one version, is based on a per property basis and focuses on signifying if the property appears to be conforming or non-conforming as well as projecting upside potential or downside risk using either public record data or user provided data. Other versions are by perspective (such as buyer, remodeler, renter, investor, seller) neighborhood or geographic area. The invention includes the ability for users to iterate using a pre-programmed calculator based on the zoning rules and includes web-based and mobile APIs.

Covenants, conditions and restrictions (referred to as CC&Rs) are widely used for condominiums, master-planned communities and planned-unit developments—all of which share the need to describe the rights and duties of the owners. While they differ from zoning in that the contract is between private parties rather than a government enforcement agency, the same principles apply to developing a data retrieval tool.

Embodiments of the disclosed invention include methods of segregating data. Embodiments of the invention include data mining and information retrieval within the typical labyrinth of zoning regulations and optionally pairs it with data providers like Geographic Information System (GIS), considered a public record source, to identify a property's zoning code (s) and retrieve criteria to assist in ascertaining if the property meets conformity standards. The availability of GIS allows for the creation of “Interactive Zoning Maps” which combine the zoning code abbreviation and/or description and/or rules and/or other available property specific information. Users can click upon a zoning map label or parcel outline to obtain information. Embodiments of the invention work with additional zoning and land-use related data such as variances, transcribing planning commissioner meeting notes, historic districts, and other factors to add to information to either a specific property or to a zoning code. Embodiments of the invention include a “distance to” aspect detailing zoning and code use allowed or not allowed within a specified number of feet from the subject property.

People don't “see” zoning and land-use controls as they are an invisible force but they see the results, which manifest into physical characteristics and changes of a place. The invention approaches the complex subject of zoning and land-use development controls by structuring both spatial and non-spatial data with structured zoning controls and logic. Prior to this invention, finding answer for a specific property, zoning control or combination was a manual, labor-intensive project requiring technical expertise. In the case of search by “use” with user determined parameters such as lot size and building size, a manual process is not feasible given the volume of variables; a database with structured data and logic is required.

Embodiments of the disclosed invention include methods of expansive search and retrieval capabilities allow answering complex questions about existing conditions and hypothetical scenarios based on the perspective of the user. An important function of the invention is addressing hypothetical and for serving multiple purposes including assessing proposed rezoning, considering rezoning a property to achieve a certain expected outcome and/or asking for the zoning district to meet the criteria of the project being proposed. Until the zoning process to approve or deny a request for a zoning permission is complete, the scenario is hypothetical. The same is true for planning which differs from zoning in that zoning is regulatory while planning is a function of gathering data, assessing conditions and making recommendations for how to change the zoning in order to meet the policy objectives.

Cities are starting to undergo rezoning. Denver and Philadelphia completed rezoning in the last 5 years. Austin is just starting the process which involves planning, measuring community sentiment about ideas and then creating the zoning regulations. Hypothetical scenario analysis is helpful for understanding existing conditions in comparison to what is being proposed or future conditions for one or more properties and/or districts with associations of financial consideration including assessed values, retail sales, property values and costs reported with permits.

The incorporation of perspective refers to motivations, role and experience with zoning in a specific jurisdiction; this can be expanded to reflect risk tolerance and real estate development knowledge for a particular property type and/or municipality. Local knowledge is powerful information, especially when coupled with community connections to gauge the sentiment of a proposed (hypothetical) development.

Zoning is the law for a particular municipality and is written in technical language. It should not be confused with land-use codes which are used at the county level for tax assessment purposes and describe broad real estate categories such as vacant land, industrial, residential. The conventional zoning is called Euclidean while newer zoning is called form-based (also called transect zones or Smartcode). Regardless of the type, the rules regulate what can be built where, at what size and how it can be used and are captured by the invention.

The disclosed invention is designed to eliminate the need to access a zoning ordinance, a zoning map, and/or a property data record when conducting initial research and leverages the ability for searching for properties using zoning controls from the zoning ordinance. It is also designed to present aspects of the zoning process currently difficult to understand given the current methodology for the announcements of proposed projects, recordation of community sentiment and searchable retrievals of the outcomes involving the public hearings relative to the query.

Detailed embodiments of systems and methods for automating land use utility through standardization are disclosed herein, however, it is to be understood the examples are to provide an idea of how the systems and methods may be embodied in different forms. Specific functional details disclosed herein are not meant to be limiting. They form the basis for teaching one skilled in the art to employ described systems and methods herein.

Terms and Definitions

In describing the various embodiments of the invention the following terms and definitions are sometimes used:

“Action-oriented” use categories include: “at work”, “at home”, “at play”, “at gov”.

Classification: zoning designation

Code: same as classification, it is a way to identify a zoning type

DIY value: one of the various names of the invention, sometimes used as “do it yourself valuation.”

Effort: refers to the number of steps needed to be taken prior to submittal of documentation to the city or county to request a use that requires a conditional or special use permit and/or permit to make physical changes to the property that require building permits.

“Everyday Recognition” adds well-known brand named that people can identify so as to make the type of use and size understood.

Friendly use descriptions: conversion of ‘expert’ terms to make understandable to the layperson

Nomenclature translation and explanation—real estate terms include square feet (sf), floor area ratio (FAR). Embodiments of the disclosed invention seek to level the playing field in the user's comprehension by offering conversion of the uses. Examples include the use “hotel” which in some zoning codes is labeled as “transient habitation”.

Reverse directory: concept of being able to retrieve information from multiple perspectives

Size: refers to development standards that include such things as minimal lot size, maximum number of stories, and density levels that refer to number of dwelling units per square foot of a lot size.

Time: refers to the actual time in days or months once required documentation concerning obtaining a use permit or building permit is submitted.

“Traditional” use categories include: “retail”, “industrial”, “office”, “residential”, “other”

User-friendly rating: scale that rates ease, cost and convenience to the property owner or renter relative to obtaining proper permissions to use a property.

Use: refers to activities: residential and businesses.

Watch list encompasses property types (by use) or related to size or some other characteristic that need to be recognized as either being no longer allowed, being allowed but only with conditional use permit or the reverse—uses that were once subject to a conditional use permit no longer are or those uses that were not permitted even with a conditional use permit but now are either with or without a conditional use permit.

Zonability: sometimes used to describe embodiments of the disclosed invention.

Zoning Calculator: refers to the development of a series of pre-set formulas developed by a specific zoning code to mimic the rules. Users can add variables such as lot size, building size, etc. and get feedback as to whether or not such imputed variables meet the preset rules and can show how this impacts the property's Zoning Score.

Zoning Flexibility Rating: a method that measures the appeal of a zoning code or classification based on its uses permitted, scale of sizes permitted and other pertinent data points.

Zoning Score: a measure for a specific property as to its likely conformity or nonconformity per the zoning rules and/or level of compatibility of a property's zoning considering adjacent properties and/or a measure of upside potential and downside risk. Data about the property is from a public record source. Users can add and edit data to see a revised “user generated” Zoning Score. A Zoning Score may also be considered a measurement the specified property conforms to the current zoning (or not) based on available data that includes public record and/or user added. The tool is able to indicate potential upside based on a series of calculations or downside risk. Upside potential would signify room to expand while downside risk suggests an overbuilt situation.

Zoning controls are the elements that are regulated about a building's size, height and/or use. For conventional zoning, the terminology is development standards, user permissions and additional requirements regulating a property's utility. For form-based codes or transect zones, the terms are building type, buildable area, height and use permissions.

Zoning controls with assessment of location change indicator data: the pairing of structured zoning controls with data associated with measuring change for a location comprising demographics, neighborhood boundaries, assessed values, zoning permits, zoning cases, building permits, retail sales, real estate sales.

Zoning compatibility: the concept applies to assessing a situation that may put prove not harmonious by adding more steps to check if a use will be permitted or building can be constructed and how. It involve a residential property adjacent to commercial, a protected use such as a church or school which require a certain number of linear feet for separation; this is also referred to as the “distance-to” rule.

Descriptor: a brief description for the the zoning control and/or policy goal in such a way a lay person can understand the concepts of encouraging or discouraging development and/or change and/or existing condition. For conventional zoning, the use is the driver for most policy such as “residential” “mixed” etc. For form-based codes or transect zones, the terminology is more descriptive such as “neighborhood main street”.

Suitable property: the concept that some real property sites are more suitable varies by the user's perceived needs as well as data showing economic conditions in one or more locations.

Building development potential: the term is also called “massing” and relates to the potential building size and height using zoning controls to express either a minimum and/or a maximum.

Use permissions potential: the regulated zoning controls regarding how a property is used in terms of the business use, building type and/or activities.

User perspective: the concept of perspective involves motivation, role and experience level. “Motivation” has to do with why the user is searching. “Role” has to do with the position of the person relative to the property such as owner/investor, owner/user, buyer, real estate agent etc. The “experience” level has to do with knowledge about zoning, real estate development and/or the zoning process relative to the jurisdiction of their inquiry.

Study and/or Character area: the area describes the building inventory in terms of physical characteristic comprising number of parcels, age of buildings, construction quality, type, ownership rate, length of tenancy, area amenities, new construction, permit levels, assessed values, job opportunities, retail, transportation and crime.

Property utility: the development and use related options for a property with a focus on the intensity of development allowed.

Property utility with hypothetical zoning controls: the possible development and use related options for a property to be developed with a focus on the intensity of development allowed.

Private-public partnerships: the formation of a partnership to construct a property or project that uses public money in some form which can vary from waived permit fees to donation of land and using the private sector to actually handle the construction and/or management duties of the property once built.

Economic utility: a focus on the property's revenue generating capacity now and in the future with projections of potential gross revenue relative to current and projected supply and demand for the use which includes its construction, building type and durable of the income stream relative to its characteristics including the surrounding area.

Economic development: the concept that policy driven initiatives such as infrastructure projects, job creation incentives, construction incentives, will build wealth for the municipality. The role of an economic developer is to develop and administer policy based on the municipality's objectives such as creating new jobs or increasing the retail sales tax base. Real estate invariably becomes a factor.

Community resource: a specific property, property type, aged property or area viewed as having value to the community that allows the community to have a voice on deciding what the property owner can and cannot do; in effect, it is another zoning control.

Community notification: the zoning process to let people know of a proposed zoning case; the notification is sent by US mail to those property owners and renters within a certain distance of the proposed project.

Community comments: a form that allows the community to write their views about a zoning case and/or speak at a public hearing.

Community benefits: developer provided incentives to the community provided in exchange for their support of the the proposed development.

Planner recommendation: an indication of how the planners view a zoning case based on their review of the case.

Future land use: a description for a property type that reflects the municipality's objectives for the location generated from future planning determinations.

Development controls: all controls on development including zoning and/or other land-use requirements including environmental, water, utilities, infrastructure, community benefits.

Comprehensive plan: a long-term plan, often mandated by the state, for a municipality to prepare to show their vision, goals and objectives to handle future growth including but not limited to transportation, housing, etc. It can be a document or series of documents and is used for making land use decisions, preparation for implementing ordinances, preparations for capital investments, and the location for future growth.

Existing conditions: the existing level of improvements, site characteristics, ownership, demographics (neighborhood character); often called an existing conditions assessment used by planners to understand the situation before applying new regulations.

Retail sales tax base: the tax revenue generated from consumers' purchase of goods and services within a municipality.

Leakage studies: an assessment as to where the sales tax revenue is coming from in terms of the customer base to identify if the local market is the being serviced or if they are spending the dollars elsewhere. Demographics matter so municipalities under a threshold size will not as easily attract well established chain retailers.

Property tax base: the properties contributing to the municipalities tax basis.

Hypothetical property value: an indication of the property's value “as if” built to the hypothetical building size, height and/or use.

Land use development controls: additional requirements beyond those found in zoning required for obtaining a zoning permit and/or entitlement. Examples include a) environmental requirements for watersheds, wetlands b) regulating plans that work in tandem with zoning and c) transportation plans with requirements for buildings and uses in proximity of stations.

Types of zoning: transect, Euclidan, form-based and/or some combination.

Land-use code: a description used by assessors to classify a property regarding existing conditions and are not associated with a set of regulations.

Location change indicator data: data providing a sense of how a place is changing in measurable terms comprising demographics, transportation options, retail sales, new construction, permit activity.

Transit-oriented development (TOD)—A mixed-use residential or commercial area designed to maximize access to public transport, increase economic activity, and often incorporates features to encourage transit ridership. A TOD typically has a center with a transit station or stop (train station, metro station, or bus stop), surrounded by relatively high-density development with progressively lower-density development spreading outward from the center.

Geographic information systems data: is spatial data geo-referenced to the coordinates of the earth. Geo-spatial and GIS are used interchangeably with the formal name geographic information system. Should this technology be replaced, the application will reflect the new format.

ExtraTerritorial Jurisdiction (ETJ): The area outside the city boundaries but identified as land a city can expand into with the power to implement zoning.

Zoning changes articulated through a process of mapping zoning GIS data with the ordinance and adding descriptors for those changes about who, what, and why and using parcel boundaries to understand the where and counts by zoning code focus over time.

Standardized land use utility categories contains one or more category names recognized in real estate to convey residential, multifamily, commercial, mixed use etc. The term “category” is sometimes used interchangeable to keep the context of sentence clear without repeating “standardized land use utility” each time.

Standardized land use utility is the standardization of multiple data sources including current property use, zoning and future land use.

Standardize land use classifier list, also referred to as “classifier list”, provides the breakouts of categories from multiple data sources including current property use, zoning and future land use are mapped to in order to create a standardization.

Subcategory standardized land use classifier list, also referred to as “sub-classifier or sub-category list”, provides the further breakouts of standardized land use categories from multiple data sources including current property use, zoning and future land use are mapped to a more refined level in order to create additional data points for standardization.

Standardize property use and/or standardize current property use are used interchangeably.

Standardized zoned for use or uses and/or standardized zoning uses are used interchangeably.

Standardized future land uses and/or standardized future land use designators are used interchangeably.

Standardized elements are the date, place and breakouts for improved status vs. vacant and references to future land use vs. current condition for zoning and/or current use are part of the invention's descriptions for search, assessment, tracking and evaluations.

Location can include any type of geographical area defined by a boundary including but not limited to: one or more parcels, places, cities, MSAs and/or economically defined areas and/or drawn area.

Property data and property records are used interchangeably.

FLU is an abbreviation for future land use and is sometimes expressed as FLUM whereby the “M” refers to “map.”

Vacant land as property use are identified as a sub-category to either standardized zoning and/or standardized future land use.

Optimal standardized land use utility is when a more intense standardized land use utility difference is identified using land use intensity hierarchy between standardized current property use and standardized zoning on a parcel and/or when a difference between standardized zoning and standardized future land use is identified on a parcel. The goal is to be able to return one standardized land use category in cases when there are two or more options. The can pertain to parcels with more than one zoning district and/or more than one future land use designation.

Optimal standardized land use confidence reflects the likelihood of achieving optimal standardized land use utility for the arbitrage opportunity parcel when compared to standardized land use utility for one or more nearby parcels and/or other identified nearby arbitrage opportunity parcels and wherein confidence score can be expressed as a numeric single point of value, range or related qualitative description.

Optional optimizations include the use of property data attributes for density and/or size.

Land Use Intensity Hierarchy for standardized land use category intensity ranks agricultural at the low end and industrial is at the high end ranking for intensity of standardized land use category and ranks agricultural at the low end and industrial is at the high end,

True standardized market supply is the complete view of supply of land with breakouts for standardized land use category or subcategory, improved or vacant status, arbitrage opportunities, by data source, by geography at a point in time and/or over periods of time.

Standardized land use arbitrage opportunity are identified by standardized land use utility by data source wherein data source is property data, zoning data and/or future land data.

Standardized land use pattern index is the tracking of changes by standardized land use utility using census data as part of the indications of change over time.

Standardized lending for residential 1-4 requires a translation from zoning that categorizes “multifamily” at three (3) units to the lending standard 1-4 dwelling units and the 5+ unit category through the use of a sub-category land use standardization process.

Data source for Land Use Utility Standardization refers to property record, zoning and/or future land use.

Further Details of the Various Embodiments of the Invention

In one embodiment of the disclosed invention, a quick glance feature or the composure of a Zoning Score may be composed by use of the following criteria:

By Use(s), to find the zoning classifications which allow for XYZ, or proposed uses of the property.

This can be general use (retail, residential, industrial, office, mixed)

Specific use (examples: dress store, barber shop, auto dealership)

Logic by grouping

Download option/report option

By Size, to find the zoning classifications that allow for XYZ.

By minimal lot size

By Floor Area Ratio (or density)

By Maximum Height (or #of stories)

Logic by groupings

Download option/report option

By Zoning classification or code, provides a summary or overview with an option to add a specific property to “test” its conformity or nonconformity based on basic principles such as:

Minimum lot size

Uses

Number of Stories

By Specific Property relies upon linking an address with a zoning code(s) that provides an overview of the property's zoning per publicly available data. This includes showing:

Zoning Code Name and abbreviated Name

Zoning Code Definition

Permitted Uses

Conditional Uses

Developmental standards

By Zoning Score, as defined above.

EXAMPLES OF EMBODIMENTS OF THE INVENTION

Example 1—a business owner wants to open up a clothing store in an area and is considering a few cities but doesn't know which zoning allows for “retail—women's apparel”. By using Zonability's technology, the user would enter the search for “retail” in the primary field, “women's apparel” or “dress store” in the secondary field to find a printout of zoning codes in a specified city or county that matches those uses requested.

Example 2—a user could use this invention to see uses by category such as “at work” and opt to select a zoning code or not. Either a single city or multiple cities can be selected. Example 2a. shows if the user specifies a zoning code and a specific city.

2a. Uses “at Work”—for Specified Zoning (C2) in (San Francisco)

-   -   Office—yes     -   Medical office—no     -   Manufacturing—no

2b. Zoning Codes for Uses “at Work” for Specified City/County (San Francisco) but does not Specify a Zoning Code so all “at Work” Uses Will Return

-   -   C2—office, bank, insurance agency     -   M1—office, school, manufacturing, auto repair     -   C3—medical office

Zonability offers a reverse directory concept so a user can look up by the end result desired or by code. This is the logic and an example for “Uses”

At home—housing, pet related (boarding), child related (day care facilities), religious, boarding homes, accessory buildings

At work—office, schools, manufacturing, medical offices, bank, insurance agency, police, private school, Laundromats, auto repair

At play—restaurants, retailers, entertainment, gyms, museums, parks, media, café, clubs, hotels, produce market, personal care

At gov—this is the catch all category for such government buildings, institutions, parks, Rotary club, hospitals

Some uses that cross between the 4 or so categories can be classified in two of the three. A park may be “at play” or “atgov”.

Regarding “Size”, this invention allows users to look up information either by floor area ratio (FAR), minimum lot size, or number of stories.

Reverse directory concept—example for “Size”

Users select the City and/or County.

Then, the user selects “Size” choice to search for specific data with pull down menu to select ranges sought for one of the three:

Floor area ratio (FAR)—pull down options with data range (1.0 and less, 1.0 to 2.0, 3.0 to 4.0, 4.0+)

Minimum lot size—pull down options with data range (2,000 sf and smaller, 2,001 to 4,000 sf, 4,001 to 6,000 sf).

Maximum number of stories—pull down options (1, 2, 3, 5 and under, 5 to 10, 10+)

The database shows the zoning classification that meets the criteria that matches. So, if a user wants to know what zoning allows for a high FAR, they might select the range (4.0+) and the result would show the corresponding zoning classifications that allow for FAR of 4.0+.

Example 3

City—San Francisco

Size Choice—FAR and 4.0+

Results:

-   -   RC-4     -   C-2-1     -   C-3-O     -   C-3-R

Other Look Ups:

By Code: Logic/Example

Zonability has taken zoning code information by assigned zoning classification (by the jurisdiction) and created a user friendly summary of the pertinent data based on the notion of satisfying the more common zoning needs.

Example 4—a user could get an aggregation of data regarding a particular zoning in the form of a widget. The zoning code, abbreviation, definition, a set of rules regarding development standards and lists of approved uses as well as conditional uses. The invention takes that zoning information that is shown on line and refers to multiple sections within the Code to show Size and Use information in a summary format and following the same concept of layperson language.

Example 5—this invention allows prominent sections of other municipal ordinance such ordinances as the “Green Ordinance”. The data is taken from the ordinance and allows for data retrieval—Chart 1 shows a synopsis of the data collected. Users can ask “what properties are impacted by the Green Ordinance”. Answer: New construction, some remodels. Or, how many points are required for 4-plexes? Answer: 50 for this year.

CHART 1 Points Points Points Storm by by by water Mkt By type By project 2010 2011 2012 mgmt? San Francisco 4 units New, some 50 50 75 Yes and less remodels San Francisco 5+ units New, some 50 50 75 Yes and/or remodels mid-rises San Francisco 5+ units New, some LEED LEED LEED Yes and/or remodels Silver Silver Silver high-rises (75+ feet)

Example 6—a user could get an aggregation of data regarding zoning. The zoning code, abbreviation, definition, a set of rules regarding development standards and lists of uses with permissions.

CHART 2 List of Other Other animals- Ok in Is a local require- require- SF R- license Qty. ments ments County zones? required? Dept. limits 1 2 Horses Yes Dept. of 1 Stable is Neighbor Public required input Health considered. Cattle No Sheep Yes 1 Stable is Neighbor required input considered. Goat Yes Dept. of 2 Stable is Neighbor Public required input Health considered. Poultry Yes “4” is the Must keep in maximum coops or number of enclosures - animals 20-foot (dogs, requirement rabbits, from cats, residence. chickens, gerbils).

By a Specific Address: Logic/Example

An address can be requested for a zoning look up. This is an extra step to the basic invention but data providers exist that links addresses to zoning classifications. The same steps outlined in “By Code” are done. Please see steps outlined in this Look Up.

See FIG. 6 Shows sample report with method to compare a particular property's check for conformity—this relies on using a public record source namely GIS, a widely recognized source for providing information like this and is often times open to the public to use in software applications.

Other Look Up & Data Retrieval Options

Parking to Find Ratio of Required Parking:

By zoning classification or code

By such words as “none”

By specified ranges

Download option/report option

Signage to Find Rules by Asking to Retrieve Information:

By zoning classification or code

By use

By size (specify range that includes billboards)

Zoning Download option/report option

Zoning in Transition—by Product is a “Watch List”

“Uses” no longer approved (by city/county, by use, by zoning classification) “Uses” recently approved (by city/county, by use, by zoning classification) “Uses” moved to “special permission” category (by city/county, by use, by zoning classification)

Down zoned properties—list by address

Rezoning—list by address

Proposals to change policy within zoning code

Carbon Trading Impacts Embedded in Zoning

Uses no longer approved (by city/county, by use, by zoning classification)

Uses recently approved (by city/county, by use, by zoning classification)

Uses moved to “special permission” category (by city/county, by use, by zoning classification)

New requirements in process for obtaining use permits

New requirements for buildings to meet compliancy standards (this includes meeting Green Ordinance standards).

Ability to model hypothetical changes to a property using Green Ordinance guidelines and recalculate its carbon impacts.

Market vs. Property energy use (gas, electric, water) as well as calculated output based on size characteristics and market benchmarks.

Ability to know the percentage of properties within a zoning code that exceed limits set for emissions.

Zoning Impacts—Peeling Away Layers of Data Available on a Per Parcel Basis:

Historic significance

Variances

Zoning as a Consumer Protection Issues—a Disclosure

Given the weight of zoning on property rights, potential property buyers may want a formal disclosure to be issued at the time of purchase.

Pre-Zoning Notification—Overview of the Process:

A property owner wants to change something that will impact the property's appearance and/or use. The zoning process requires public notification in order to gather outside feedback and weigh what is being requested to what has already been approved in the past under similar conditions.

Using this invention, property owners can create their own notification to let those within a specified distance know about potential plans to alter a property and to gather feedback as well as attempt to generate some consensus building.

Posting photos of similar projects, uploading conceptual drawings/plans, showing analysis for how the plan works with the current zoning—essentially providing a way to explore the natural process that already happens when submitting a plan requiring public approval on a small scale.

The present invention overcomes shortfalls in the known related art by presenting unobvious and unique methods of breaking down zoning code information that is written in either a sentence structure, table format that references other sections of the code and creating individual fields of the data. By breaking down the data, this allows each piece to be viewed individually and allows for users to retrieve specific information as well as it allows for the grouping of data.

This invention also allows for the export of the data in either a printed report format (PDF) and/or a spreadsheet (.cvs) format.

Side-by-side comparison of zoning—this would allow users to see what are the differences between various zoning codes and/or properties.

Measuring the zoning code's flexibility can add insight into the value of a property's location.

Providing a Zoning Score gives context to the way a property property's conformity or non-conformity

Linking an address with a code and providing a calculation to see where the property conforms or not. This relies on a data such as GIS.

Combining the zoning ordinance data with other related data including carbon trade requirements, a building code issue, whether generated through automation or input by user can deliver a more robust and complete picture of a property's liability as it relates to some government controls.

Segmenting relevant CC&R data for a specific building, complex or master-planned community allows for easy data retrieval regarding rules and financial issues included in the CC&Rs such as a) pet policy b) process to make changes to a unit c) hours of operation d) financial health of the Association e) inclusion of expenses in the dues. Other relevant issues that apply to a broad audience of existing owners and potential buyers could be included in this list.

One of the main advantages of this invention is that is makes something currently obtuse to a layperson practical and “user friendly”. The rating and scoring system put a measurement to a process or concept that currently doesn't have one currently, adding to the user's comprehension.

In another embodiment of the disclosed invention, multiple agency and/or department data sets are fused with segmenting the zoning ordinance data into a database to allow for a user to understand the layers of regulation applicable to the parcel or parcels. When displayed on an electronic map, color coded options can be added to make distinctions about the districts readily visible. The user is able to identify a property and/or location with a zoning district or districts and additional property information.

When using the interface associated with the database, the user is able to retrieve information about a zoning district as associated with parcels within the district. This replaces the need to associate the layers of regulations in addition to the zoning ordinance for simple search and retrieval needs. It provides for a self-service tool useful to government departments, private planners, real estate professionals, economic developers.

In another embodiment of the disclosed invention, site selection become automated by the use of paired segmented zoning ordinance data with geo-spatial zoning data and property data to find locations suiting a user's needs comprising:

Use of the site with permissions as regulated by the zoning and/or land use development codes;

Development potential for the parcel as regulated by the zoning and/or land use development codes;

Property record data for the parcel or parcels identified.

In yet another embodiment of the disclosed invention, a user is able to run hypothetical scenarios to better understand the impacts of zoning actions on proposed developments for a specific property and/or area and/or district comprising:

Rezoning, Change of use, Change of development standard

Examples of Embodiments of the Invention

Example A, a prospective buyer is interested in a property and wants to know the zoning using a mobile device. The invention includes a map to use for search which allows the user to either get geo-located or use the menu to enter and address and/or property identification number from the assessor to locate the property. Once located, the user is able to visual see the zoning associated with the location comprising district abbreviation(s) and/or name(s) and/or descriptor(s) with property data comprising the improvements, ownership, existing use, year built. Color coding the map provides more context to the user relative to a zoning descriptor and/or policy and/or control and/or status.

Example B, a business owner wants to find a new location that meets the business' need for a restaurant. The ideal location is along a well-known street that extends between three municipalities. The invention allows for this type of search. First, the user identifies the use wanted which is restaurant. Next, the location is identified by drawing a polygon that includes the corridor containing parcels within three municipalities. The user can opt to run a search with this criteria or add other search variables comprising a) property traits such as a range for lot size, a range for existing building size, a range for year built; and/or b) rule traits such as potential building size, potential building height, potential number of units. The data retrieval associates the zoning controls allowing for a restaurant use with a property. The results can be returned and the user can iterate and/or filter by changing a search parameter. Results can be paired with location change indicator data. This provides more context about the locations retrieved and comprises demographics, permits, retail sales, transportation proximity, workforce accessibility.

Example C, an architect has a client asking about locations to allow for a three-story, 30,000 square foot medical office building in zip code 78704. The invention allows the architect to search for the proposed development parameters including a) potential building size b) potential building height and c) use. The results can be returned and the user can iterate and/or filter by changing a search parameter. Results matching the criteria are returned can be paired with location change indicator data. This provides more context about the locations retrieved and comprises demographics, permits, retail sales, transportation proximity, workforce accessibility.

Example D, a property owner receives a notice in the mail announcing the upcoming public hearing for a proposed rezoning of a property in the neighborhood located within 500 feet, the prescribed linear feet requiring notifications to property owners and renters. The notice includes an address, the existing zoning abbreviation and name as well as the proposed. It includes a description of the proposed development; however, the user wants to see a before and after snapshot and try to understand the implications should the rezoning be approved. The invention allows the user to run a search for the existing conditions. Then, it allows the user to run a search with hypothetical conditions in which the property is identified and the proposed zoning controls are applied. The data retrieval reflects the zoning impacts including calculations for potential development standards comprising building size, density and height and potential use permissions. The data can be paired with zoning permit and zoning case data to provide additional context as to the uniqueness and outcomes from similar zoning actions.

Example E, an investor has narrowed down the options for an upcoming purchase to two properties and wants to understand the development potential. By using the invention, the structuring data including zoning and land use development code data with logic provide a reasonable idea of each property's development potential and use without having to undergo a feasibility analysis. Adding a proforma reflecting the property potential, allows the user to run a financial analysis on the “as if” built then deduct an estimate for construction costs to get to an “as is” property value indication.

Example F, a realtor has a prospective seller and is doing research to use as part of the listing proposal. The invention allows for looking up a property to understand its development potential and use. In this case, the property is improved with an older home in an area transitioning to a demographic wanting more compact homes. After entering a search criteria to identify the property, the invention generates a report with the structure zoning controls returning several types of residential uses shown as likely permitted including patio homes, attached townhouses and residential condominium. The development potential uses the zoning control for maximum height to return a 4-story building. The report also generates answers for neighboring properties. With this information, the realtor is able to see the subject property has an advantage over an existing listing down the block which limits the types of residential to detached house or duplex and only allows 2-story buildings.

Example G, a site locator is looking for a new location for an expanding chain of bars. The use “bar” is one regulated at the State level in order to obtain a liquor sales license permit. The requirements, per the local zoning ordinance, mimic the State level's requirement that no new bar establishment be located within 1000 feet of an existing school or church. The invention is able to model the zoning compatibility using logic that retrieves only records for properties that allow the use “bar” and are not located within 1000 feet of an existing school or church. This functionality can be expanded to include other uses and reversed to allow a user to identify locations of what they perceive as “incompatible” uses within a specified distance from an identified starting location. These could reflect existing and/or potential uses.

Example H, a prospective homebuyer wants to understand the location of the home and thinks it is wise to check for zoning and/or land-use development controls to see if the area is uniformly regulated by zoning and/or land use development controls. The applications saves the user from having to go to multiple agency websites to look up the same location to see which regulations apply to the property and its location within, or not, the city boundary.

Example I, a site consultant is hired to look into the economic feasibility for a proposed Target store and is interested in building an analysis that includes the future zoning plans for the area to better understand the long term economic potential for developing in the proposed location.

Step 1. The user identifies the type of search from a menu, or types a search or clicks on an image to express “future planning.”

Step 2. The user identifies the location for a property search with options comprising allowing the user to get geo-located or enter an address or addresses and/or property identification number(s) from the assessor to locate the property. Once located, the user is able continue with search.

Step 3. The user selects what area to include in the assessment of “future planning” and is able to draw a polygon, enter addresses and/or property identification numbers assigned by the assessor's office or specify a buffer of “x” feet or miles.

Step 4. Data retrieval includes the development potential for the identified properties and identifies future land use with the option to map outputs color coded by future land use

Automated Site Selection—Example J

A business owner is interested in finding a new location and would prefer to know all potential locations for the use, which is “hotel,” in one of 3 cities with the criteria being a 100,000 to 150,000 square foot building to re-purpose.

Step 1. The user identifies the use for “hotel.”

Step 2. The user identifies the locations of interest by identifying the jurisidiction names from a list or clicking a map.

Step 3 A search box allows the user to identify the parameters for the search for physical property traits reflecting the 100,000 to 150,000 square foot building size.

Step 4. Data retrieval shows locations matching search criteria with property specific information comprising the property data, zoning, the use requested “hotel” and the permission for all 100,000 to 150,000 square foot buildings in the 3 cities requested.

10. Automated Site Selection—Example K

An architect wants to build a custom home for his client with the requirements it be located in zip code 78704 and a lot of at least 2 acres to ensure room for either a swimming pool or tennis court.

Step 1. The user identifies the use “single-family home”.

Step 2. The user identifies the location, zip code 78704.

Step 3. The user identifies the search parameters to include the lot size parameter of 2 or more acres.

Step 4. Data retrieval shows locations matching the parameters for the search for properties in 78704 with at least 2 acres allowing the use “single-family home.”

Example L, a planner wants to get existing conditions for a study area, which is something done as a step to assess a location before making policy changes and/or a zoning process decision.

Step 1. The user identifies the type of search from a menu, or types a search or clicks an image to express “existing conditions”

Step 2. The user identifies the location for an area with options comprising zip code, neighborhood, draw a polygon, enter in property identification number(s) from the assessor, and/or enter in address(es) or specify a buffer of “x” feet or miles from an identified property.

Step 3. Data retrieval shows existing conditions for the parcels identified comprising number of lots, median lot size, median building age, ownership type, ownership length, zoning districts.

Example M, “What If?” Zoning—Example 1

A hypothetical scenario testing in which user can search and retrieve differences between two zoning districts as relates to an identified property. A student wants to know the difference between MF-3 and MF-6 zoning as it might apply to a vacant lot.

Step 1. The user identifies the type of search from a menu, or types a search or clicks an image to express “district-to-district”

Step 2. The user identifies the jurisdiction

Step 3. The user identifies the two zoning districts from a menu of all zoning districts in the specified jurisdiction.

Step 4. Data retrieval shows the district descriptors and controls comprising name, abbreviation, development standards and uses as they apply to the identified property which include a calculated development building potential and uses specific to that location.

Example N, “What If?” Zoning—Example 2

A hypothetical scenario testing in which user can search and retrieve impacts on a specified property by changing a physical attribute. A developer wants to know what would be the impact on the property's zoning controls if the lot were 10,000 square feet.

Step 1. The user identifies the type of search from a menu, or types a search or clicks on an image to express “hypothetical—property”

Step 2. The user identifies the location for a property search with options comprising allowing the user to get geo-located or enter an address(ed) and/or property identification number(s) from the assessor to locate the property. Once located, the user is able continue with search.

Step 3. The user is asked to select which property attribute to change. In this case, the user identifies “lot size” from a menu.

Step 4. The user is then asked what size lot to run the analysis on and the user enters the hypothetical size.

Step 5. Data retrieval shows the impact on development potential and use if the lot were 10,000 square feet.

Example O, “What If?” Zoning—Example 3

A hypothetical scenario testing in which user can apply a change in a development standard for a specified area in order to obtain retrieve the results for the maximum building size potential for the identified properties in the identified area. A planning commissioner wants to know what would be the impact on maximum potential building size if the commissioners approved an overlay zoning district to change the height maximum from 2 stories to 4 to an area.

Step 1. The user identifies the type of search from a menu, or types a search or clicks on an image to express hypothetical change.

Step 2. The user identifies the location for an area with options comprising zip code, neighborhood, draw a polygon, enter in property identification number(s) from the assessor, and/or enter in address(es) or specify a buffer of “x” feet or miles from an identified property.

Step 3. The user identifies the hypothetical change by selecting from a drop down menu, choosing an image, or typing, changing the development standard “height”.

Step 4. The value of the development standard is added, 4 stories.

Step 5. Data retrieval includes existing conditions and the development potential for the identified properties using the current 2-story maximum and the 4-story maximum which reflects calculations for potential maximum building size.

Example P, Hypothetical scenario testing in which user can retrieve information about future zoning plans for a specified area. A site consultant is hired to look into the economic feasibility for a proposed Target store and is interested in building an analysis that includes the future zoning plans for the area to better understand the long term economic potential for developing in the proposed location.

Step 1. The user identifies the type of search from a menu, or types a search or clicks on an image to express “future planning.”

Step 2. The user identifies the location for a property search with options comprising allowing the user to get geo-located or enter an address(ed) and/or property identification number(s) from the assessor to locate the property. Once located, the user is able continue with search.

Step 3. The user selects what area to include in the assessment of “future planning” and is able to draw a polygon, enter addresses and/or property identification numbers assigned by the assessor's office or specify a buffer of “x” feet or miles.

Step 4. Data retrieval includes the development potential for the identified properties and identifies future land use with the option to map outputs color coded by future land use.

Example Q, descriptors identify building general, not exact, height controls.

To get a sense of a building height in a general sense, not exact, it is useful to have standardized data designed for this purpose.

Step 1. Import GIS zoning and parcels into a database.

Step 2. Map data from the corresponding zoning ordinance about the zoning districts and corresponding building size requirements.

Step 3. Apply a series of descriptors meant to articulate and standardize the zoning both from its type, focus and building size descriptors.

Step 4. Run a search based on criteria focused on building height descriptors (low rise, mid rise, high rise).

Step 5. Data retrieval returns parcels matching criteria.

Step 6. To further refine, pair results with recently constructed buildings identified by zoning.

Step 7. Reverse engineer those development standards from the newly constructed building.

Step 8. Apply descriptors (low rise, mid rise, high rise) to outputs from Step 6.

Example R, Why did the zoning change?

When zoning changes, it changes the way a place looks. Changes may be initiated by the property owner, city or another party. Being able to understand not just where the changes are but why and who are behind the changes makes this so useful. Before being able to explain the reason for a change, it is necessary to first discover the change. The majority of jurisdictions managing zoning, like an incorporated city, don't publish a data set articulating zoning changes. Understanding why there was a zoning change is useful information to have and is not currently available without a difficult manual process. Rezoning may be due to the owner wanting to build something that isn't allowed under the current zoning and will go through the process of a zoning change for a specific project. Other times, zoning changes due to the implementation of a city's comprehensive plan.

Step 1. Create a baseline time period for the zoning and parcels by importing GIS zoning and parcels into a database with a date of data.

Step 2. Manually association of key descriptors from the zoning GIS to the corresponding city or jurisdiction zoning ordinance.

Step 3. Assign one or more descriptors to each zoning district for zoning district type, land-use development focus and/or land-use/development policy focus.

Step 4. Intersect GIS zoning data with parcels to create a baseline time period showing zoning for specific city or jurisdiction with a breakdown of parcels by one or more descriptors.

Step 5. Repeat Step 1 but with more current GIS zoning and parcels and date the data.

Step 6. Identify parcels that show as having had a change in key descriptors for between the time periods.

Step 7. Map descriptors associated with the reason for zoning changes as applicable to the parcels identified in Step 4. The descriptor list for the change is consistently used by city.

Step 8. Run a search based on criteria focused on zoning change (who, why, when and/or where)

Step 9. Data retrieval returns parcels matching criteria.

Example S, how a zoning index is useful.

Zoning changes can be stealth. They happen in cities across the country but cataloging these changes in such a way that creates consistency between cities requires an index. This is due to the significant differences between zoning by individual city. An index allows for the comparison—over time—in a uniform fashion.

Step 1. Create a baseline time period for the zoning and parcels by importing GIS zoning and parcels into a database with a date of data.

Step 2. Manually association of key descriptors from the zoning GIS to the corresponding city or jurisdiction zoning ordinance.

Step 3. Assign one or more descriptors to each zoning district for zoning district type, land-use development focus and/or land-use/development policy focus.

Step 4. Intersect GIS zoning data with parcels to create a baseline time period showing zoning for specific city or jurisdiction with a breakdown of parcels by one or more descriptors.

Step 5. Repeat Step 1 but with more current GIS zoning and parcels and date the data.

Step 6. Identify parcels that show as having had a change in one or more key descriptors between the time periods.

Step 7. Compare changes to identify the percentage of parcels by land-use/development focus for each time period.

Step 8. Create an output of data.

Standardized Land Use Utility builds a new foundational data set expressing location now and in the future. Examples from buying or selling a house, applying for a property related loan, assessing market conditions and supply of land, predicting potential for change, enhancing location land use knowledge and finding arbitrage opportunities are part of this invention.

To standardize means mapping “use” from multiple sources and then being able to identify location to the standardized uses. An application that can process files, standardize, connect data and build a graphical user interface with unique queries are all part of this system and method disclosed herein. The option to optimize, to weigh, extend technology improvements are part of this application.

Unexpected results include:

Additional search engine criteria to be used to find property

Interactive Zoning Map

Auto-generation of Letters of Verification

Economic Development Department tool to match “use” with available space

Infill Housing

Zoning Flexibility Rating

Zoning Score

Carbon footprint tie-in with Green Ordinance

Covenants, Conditions and Restrictions (CC&R) data retrieval

Zoning Index—standardized access to zoning codes over a myriad of local governments

Multi-sourced regulations impacting a parcel or parcels

Zoning district data organization by segmented data

Site selection for a use with permission as per local zoning for one or more jurisdictions.

Hypothetical scenario testing district, property, use permission and/or development standard changes.

Compatibility for select uses requiring distance-to, adjacency.

Auto generated planning capacity

Infill development opportunity finder

Allowing users to read, write and edit data to allow for crowd sourced data capture and maintenance including sharing knowledge about specific properties and/or districts and/or uses and/or zoning controls and/or CC&R documents and/or private deed restrictions.

Providing search and retrival requirements for subdividing property, design elements, soils condition, build-able lot, infill development, parking and signage.

Associating water-related management with zoning and land-use development controls.

Associating carbon tax credit requirements in the event they are regulated by a use and/or locations regulated by the local zoning.

Progress, monitoring and enforcement of policy created through zoning.

Monitoring of individual properties, groups of properties for changes in development controls with financial impacts.

Question/Answer search and retrieval for wide range of property development and use-related questions.

Conversions of data to metric system and applying language translations in order to make the outputs relevant to people with different preferences.

Associations of metrics with zoning controls that translate the potential building size and/or units to economic and/or demographic indicators. Examples include a) number of people per apartment type b) retail sales for a use such as a fast food restaurant c) number of jobs per square foot of a research and development facility.

Associations of new trends in real estate development relative to the zoning ordinance to create search by “new trends” which can cover a wide range of topics about lifestyle, energy, transportation and urban environments.

Transit oriented development (TOD) automation in which the center for the plan is used as the basis for search to monitor inventory of available parcels and buildings per the TOD classification system consisting of the concentric circles surrounding the station.

Transect zoning automation in which the area search provides details about the existing conditions which can be paired with the “rural-urban” categorization system which covers 6 zones.

Creating the basis from which a pro forma for a development using outputs from the invention can be incorporated with the objective of associating a potential property value to the property's utility, an important need for investors.

Additional search to include potential for area change using location change indicator data, property data and structured future planning data.

Additional search to include policy zoning descriptors to align lifestyle fit with properties being retrieved.

Creating land value estimates using hypothetical search and retrival since the property's utility coupled with economic conditions creates an “as if” built potential from which a hypothetical cost of construction and sales price can be deducted to represent a land residual value.

Assessor valuations using mass appraisals can use the technology to better understand the utility the zoning affords the properties.

Monitoring community sentiment about specific properties in their community that could be candidates for redevelopment or development, building types they support or not, businesses they support or not, zoning policy such as preservation, design etc. they support or not.

2-to-3-way communication with the consumer and experts, with option to add 3rd, government agencies, to learn more about the potential to change a property or area and/or gain and connect with local technical expertise.

Pre-development submittals are used by larger scale projects and allow experts to meet with a municipality before submitting a project to gain more insight into what their chances are of the project getting approved and/or to obtain zoning process expertise. This invention could allow for predevelopment submittals for any scale project to be uploaded to the application and shared to get peer consumer and/or expert feedback. The application is able to track data such as published pre-development submittal meetings as part of the zoning process module. Municipalities often publish the parties who have come to the municipality seeking pre-development services in order to provide transparency. The information published comprises names, addresses and/or locations, proposed use, timing and proposed building size.

Identifying properties with development incentives including being able to build in excess of the zoning, pay less tax or obtain favorable financing. Examples include: affordable housing bonus densities, locations within enterprise zone, and properties within transit-oriented development zones that qualify for favorable federal financing to purchase and develop in such districts.

Establishing where in the market a neighborhood is relative to the market cycle (revitalization, growth, maturity, decline) using data analytics from location change indicator data paired with character area. Information such as this is helpful when making policy or investment decisions.

Detecting risk associated with zoning and non conforming uses and/or buildings for purposes of properly insuring, planning and notifying interested parties.

National database of zoning district abbreviations, name, municipality, zoning policy goal and/or descriptor.

National database of zoning district uses with association to user-friendly display names and/or investment categories.

National database of Planned Developments with reverse engineered data about what got developed.

National database of zoning actions comprising location, type of zoning action, date, result with reverse engineered data about the property's existing condition.

National database of chain store real estate requirements comprising lot size, building size, building footprint, demographics.

National database of approved affordable housing projects with reverse engineered data about what got developed.

Predicative analytics for measuring the pace of change for a specified area and/or jurisdiction and/or region using the zoning process data with changes, by date, in building inventory as captured by invention with the type and use of building, size, height with demographics and/or location change indicator data.

Algorithms for property tax assessments using the basis of the amount of inventory non-taxable relative to the changes in demographics, zoning and building permits.

Virtual zoning charettes tool in which participants can interact with data to understand existing conditions relative to hypothetical or proposed changes and express an opinion.

Districts for other subject areas with real estate impacts including forest or wilderness plans, parks, fire, even zoning for the ocean is being discussed. As long as there identifiable districts associated with a place that ties a place to a regulated use and/or development, this invention can work.

Adaptive reuse tool that identifies similar “before” situations and solutions applied. An example might be a mall that was re-adapted for a use as a community center with associated retail services.

The creation of a checklist for experts to contact regarding how to achieve the development potential for a property and a way to communicate using the application to manage the pre-feasibility process.

Providing information about development potential in the ExtraTerritorial Jurisdiction (ETJ) comprising subdividing, utilities, restrictions and new developments with reverse engineered development standards to provide a sense of building mass and/or use(s) in the ETJ.

Monitoring, Communicating, Searching, Data layering

1. Zoning Flexibility Rating: the creation of a standardization system for rating zoning flexibility based multiple layers of data.

Steps Necessary to Create a Zoning Flexibility Rating

Step 1: Prominence of zoning code in particular market

Step 2: Number of uses permitted by a particular zoning code

Step 3: Number of uses requiring a conditional use permit

Step 4: Size related rules with an emphasis on density and FAR

Step 5: Exclusions and exemptions based, in part, on the number of other sections within the code referenced.

Step 6: Parking requirements

Step 7: Design review requirements

Step 8: Rebuilding options in case of destruction

Step 9: Bonus densities

Step 10: Energy related controls through such options as a “Green Building Ordinances” and/or reporting requirements for energy use, used for carbon footprint calculations.

Step 11: Moratorium uses/encouraged uses

Objectives: A Zoning Flexibility Score provides a way to measure scarcity for an existing stock of buildings as well as to project future opportunity based on the zoning for vacant land.

2. Zoning Score: creating of a score to indicate what a property's zoning is relative to the existing (or planned) project.

Steps Necessary to Create a Zoning Score

Step 1: Set of development standard rules for a particular zoning code Step 2: List of uses permitted for a particular zoning code Step 3: Size characteristics with an emphasis on lot size, building height, project density and overall size (floor area ratio—FAR). Step 4: Other physical traits including year built, last remodel, parking and construction type.

Objectives: A Zoning Score provides a 0 to 100 score articulating the degree to which a property appears to be conforming to the existing zoning code rules as well as provides for upside potential, which would be the case for a small house on a large lot that could (per the zoning code) be theoretically 50% larger in size. It also measures the downside risk, an example might be a four-plex in a zoned area for only single family homes.

The formula:

Each development standard from the local zoning code is procured.

The initial set of calculation is to measure the public record data with the zoning code rule.

A determination is made if the property appears to meet the code requirement. For each market the data is set to allow for a margin of error. An example is lot width. The zoning code might specify 25 feet. The margin for error may be set, for this variable, to be within 3 feet based on the market's averages.

For properties that appear to be undersized, the results show “room for potential expansion”. However, this invention models the intent of the zoning rules. For instance, if a property is only one-story while the maximum height for that zoning allows for a two-story structure but the building size exceeds the amount allowed then this invention will show results similar to “this property appears to be at its maximum size based on its square footage.”

Other examples might be for a property with adequate attributes that appear to meet the development lot standards except for parking. In such an instance, this invention will show results “property appears to be conforming except for the parking requirement.”

The following charts (Charts 3-5) show the sequence of how data is used for the Zoning Score, a key part of this invention. Based on the development standards criteria such as lot size, width etc. each variable is weighted. This weight is developed from a) availability of public data b) importance by market.

The User Zoning Score includes data added by the user. This invention includes the preset calculation functions to measure the likelihood the property conforms or doesn't and potential upside potential as well (expansion potential) and downside risk (signaling overbuilt conditions).

The 2nd column shows how the zoning ordinance evaluator rates that data by level of importance which is pre-set by market. The 3rd column is used for the user-generated Zoning Score and reflects the rating system, again by market, for each criteria.

CHART 3 Level of importance- Level of importance- Data point Zoning Score User Zoning Score 1 Minimum lot size High High 2 Minimum lot width Moderate High 3 Floor Area Ratio High High (FAR for short) Density-lot size per Residential 4 Unit (Equals Maximum Moderate Moderate # of Residential Units) 5 Setbacks-front Low Moderate 6 Setbacks-rear Low Moderate 7 Setbacks-side 1 Low Moderate 8 Setbacks-side 2 Low Moderate 9 Open space Low Moderate 10 Height in Feet Moderate/Low Moderate 11 Parking Moderate/High High

Chart 4 shows how the programming assigns a “true” for meets zoning rule or “false”. It also shows the flexibility within the pre-set calculations for variables such as lot size which the user can override, meaning edit for the user generated Zoning Score.

CHART 4 ASSESSOR ASSESSOR DATA: Lot DATA: Lot RULE per size in sf width (user zoning: (user can RULE per can over Conforming override zoning: ride and/or lot size size) OUTCOME lot width GIS) 5000 4500 TRUE 25 25 Formula: Formula: RULE = RULE => or > than than Assessor Assessor size Lot Width Data OUTCOME Minimum lot 5000 TRUE size Floor Area Ratio 1.8 (FAR) Density lot size 500 (sf)/unit Setback-front 10 Setback-rear 25 Setback-side 0 Parking/unit 2 Minimum lot 25 TRUE width Max height in 3 TRUE stories Minimum lot size

3. Carbon Footprint Tie-in with Green Ordinance

The adoption of Green Ordinances by communities is like any other zoning ordinance in terms of its control and directing for how people use their properties. Each community's Green Ordinance can have different sets of regulations that give local government controls. Using the same data retrieval methodology regarding zoning codes, a user can find out when, where and how the Green Ordinance impacts a property.

Furthermore, benchmarks set by a community can be included in the data to acts as a comparison for a specified property's current energy performance. Given the invention's design to pre-set calculations using the local Green Ordinance, users can run scenarios showing how improvements in energy efficiency change the property's carbon footprint.

4. CC&Rs Data Retrieval

Every condo unit, townhouse or restricted property such as those found in senior master-planned communities have an additional set of rules and regulations. While the local zoning ordinance supersedes the CC&Rs, knowing the rules within the CC&R document is important for maintaining a harmonious and well-functioning community.

This invention uses a similar technique outlined for data retrieval. The emphasis is breaking down the CC&R document so that users can easily do look ups regarding such things as owning pets, what can go on the balcony, window coverings, hours for construction, what is included in the Association dues, financial health of the Association, next Association meeting and current leaders' contact information.

5. Zoning Index—Standardized Access to Zoning Codes Over a Myriad of Local Governments

This invention allows for analysis of zoning codes from different local governments—those within close proximity, similar in population, location etc. The ability to perform this kind of analysis warrants the creation of an Index, one that measures current, historical and projected performance. Questions that can be answered include: What do the local government's most popular zoning indicate? Stability. Consistency. Transparency. Exemptions. Restrictions.

6. Monitoring, Communicating, Searching, Data Layering

This invention allows for monitoring of both a local zoning code as well as by specific property.

It opens the channel for communicating between citizens and government agencies using the API, a bi-product of this invention, or through a customer relation manager using pre-programmed questions.

Searching for properties by a specified “use” as found in a zoning code ordinance is unique.

Data layering reflects adding other sources of geo-coded data to further enhance what is allowed, restricted or being decided as well as inquires received regarding a property's zoning (these could be variances denied or approved, proposed uses or changes, previous zoning or restrictions.)

7. Multi-Source Data Consolidation—Allows for One-Stop Review of Data to Get Initial Property Characteristics with Development Controls.

Data pertaining to zoning, land-use development and property record data is created and maintained by multiple departments and/or agencies. By consolidating the data into one database, associating a district to a classification that displays on an electronic map and/or descriptor, it is possible to automatically identify the controls as it relates to a parcel and also see property data information simultaneously.

Steps Necessary to Create a Multi-Sourced Data Record

Step 1. The spatial data from the various agencies is gathered, standardized and imported into a database. The data comprises zoning (base and overlay), lots and parcels, planning areas, city and/or county boundaries, flood zone, enterprise zones, historic district boundaries, watershed and/or creeks, transportation data and future land use.

Step 2. The property record data comprising information about the property's improvements, ownership, existing use is imported from the county level agency, the assessor's office.

Step 3. Data is transferred or imported into database comprising district abbreviation, district name, descriptor and/or agency.

Step 4. An interface allows access to run queries with data returns being an electronic map, a report, API and/or data.

Step 8. Automated Study Area—allows for existing conditions regarding real property to be gathered for analysis and use prior to making proposed changes to zoning policy and/or before presenting a proposed project to a group of experts. It is designed to help the user make an informed decision about what development might be needed for a location and why.

Steps Necessary to Create an Automated Study Area

Step 1. The spatial data from the various agencies is gathered, standardized and imported into a database. The data comprises zoning (base and overlay), lots and parcels, city and/or county boundaries, flood zone, enterprise zones, historic district boundaries, watershed and/or creeks, transportation data and future land use.

Step 2. The property record data is imported from the county level agency, the assessor's office.

Step 3. Data associated with changes for a location comprising demographics, neighborhood boundaries, zoning permits, zoning cases, building permits, retail sales, real estate sales is imported into a database.

Step 4. An interface allows access to run queries with data returns being an electronic map, a report, API and/or data.

Step 9. Automated Site Selection—ability to find locations for businesses and/or development types using the combined structured data with zoning controls and logic articulating requirements for uses with permissions and development standards data that can be used to calculate potential building size and height (building mass) with the property record data. In effect, the user starts with the answer they want to achieve and ask the application to find location suitable for their request using structured zoning and property data.

Steps Necessary to Create Automated Site Selection Data Retrieval

Step 1. Importing geographical information system spatial data into a database comprising lots and parcels, zoning for base and overlay districts, city and/or county boundaries, special planning areas, watersheds, transportation plans in order to associate current zoning and/or land-use development controls on one or more parcels;

Step 2. Importing or transferring zoning controls into database to segment the data comprising district development standards related to individual zoning district basis into a database comprising of lot size and dimensions, floor area ratio, density of units, height requirements, yards or setbacks, parking;

Step 3. Importing or transferring regulated uses with permissions for individual zoning districts into a database comprising identified uses associating each with a permission as identified by the specific zoning district within the jurisdiction such as “permitted” “conditional” and “prohibited”;

Step 4. Importing data for real property into a database;

Step 5. Standardizing the data and applying business logic to ensure the data returns for development potential and use permissions reflects the hierarchy of the multiple layers of regulations on a property to allow for the district with the controlling power to determine building mass and/or use to reflect in data returns;

Step 6. Data returns are based on the search query regarding site selection with the options being either a use such as a business and/or building type or a desired building size with search parameters allowing for the user to identify a range of lot sizes and/or existing building sizes and/or year built and/or existing use(s). The concept is the data is structured and can be used for a search with the end result in mind rather than starting with a specific location.

Step 7. The user must identify the location for the search which can expand over more than one jurisdiction and can be identified by drawing on a map, which is applicable for corridors spanning over multiple jurisdictions and/or by zip code and/or within radius of specified location.

Step 8. An interface allows access to run queries with data returns being an electronic map, a report, API and/or data.

Step 10. Zoning Compatibility—The rules for zoning include details for protecting certain uses such as residential, schools and churches and/or uses that generate traffic which surpass the baseline zoning regulations for use permissions and development standards with the exception of entertainment districts. The invention allows for the structuring of rules to reflect such conditions and then uses spatial data and data for locations of select uses to provide location suitability by addressing zoning compatibility. This could also be reversed to include uses that people may perceive to be problematic such as check cashing, pawn shops and adult entertainment which, if they were located prior to the development of a church and/or school, are allowed to continue to run their businesses.

Steps Necessary to Create Zoning Compatibility Logic

Step 1. Importing geographical information system spatial data into a database comprising street type, traffic counts, entertainment districts, desired development zones, redevelopment area.

Step 2. Importing data to identify protected uses and/or areas associated with compatibility comprising schools, churches, residential neighborhoods. Within the database, associate the uses with a classification code to articulate “protected use” and those uses that “cause protection check” namely gas stations, drive-through services, liquor stores, bars.

Step 3. Transferring rules expressing compatibility requirements comprising buffer areas, distance-to rules, adjacency use, nearby uses, adjacency zoning district(s), street type by identifying zoning districts and/or planning areas into structured database with IF/THEN statements articulating conditions. The following explains the details:

i) Retrieved results for a query are identified to which the compatibility scan is to be applied.

ii) Each result has a parcel boundary from which a distance-to buffer can be run. The distance to requirement is programmed.

iii) Logic written in the form of a IF/THEN statement such as IF “protected use” is within “x” feet from the parcel boundary THEN return the result TRUE/FALSE.

Step 4. Applying zoning compatibility logic allows user to opt to remove

10. “What If?” Zoning address hypothetical scenario testing—Given the number of rezoning cases, amendments to rules controlling development standards and/or uses and the creation of new districts, it is important to test the potential outcomes. The application, through structured zoning and property data, is able to let the user see the outcomes when the a different zoning district is applied to the same property, or a use permission is changed which could impact the property's development potential or changing one or more of the development standards which could impact the building size, use or both.

Steps Necessary to Create Automated Hypothetical testing

Step 1. Importing geographical information system spatial data into a database comprising lots and parcels, zoning for base and overlay districts, city and/or county boundaries, special planning areas, watersheds, transportation plans in order to associate current zoning and/or land-use development controls on one or more parcels;

Step 2. Automatically obtaining data for real property and importing the data into a database;

Step 3. Importing or transferring zoning controls into database comprising district development standards related to individual zoning district basis into a database comprising of lot size and dimensions, floor area ratio, density of units, height requirements, yards or setbacks, parking

Step 4. Importing or transferring permissions about uses related to individual zoning districts into a database comprising of the list of identified business uses, building types, activities and associating each with a permission as identified by the specific zoning district within the jurisdiction such as “permitted” “conditional” and “prohibited”;

Step 5. An interface allows access to run a hypothetical query which entails selecting a hypothetical zoning action comprising rezoning, changing a use permission, changing a development standard.

Step 6. The user selects one or more variables to test outcomes based on the hypothetical situation comprising a zoning district, a permission for a use, a value for specified development standard;

Step 7. Retrieving data pertaining to search criteria the retrieved data to include a comparison of existing zoning controls, for the one or more properties identified in the search, to outcomes for the hypothetical scenario in regard to reflect how the property (or properties) might be developed and/or used by running calculations; and

Step 8. Creating an output comprising data, API, report, visual display of data returns on electronic map.

Standardizing Land Use Utility—handled by system and method that designed to:

Step 1. Import data from multiple data sets into database—property data, GIS data for parcel boundaries, GIS data for zoning and/or future land use;

Step 2. Intersect GIS parcels with GIS data for zoning and/or future land use to derive districts and/or designators that represent zoning and/or future land use and can be related to either zoning uses and/or future land use uses.

Step 3. Standardize data related to a) “current property use” for property data, b) zoning district uses to zoning data and/or c) future land uses to future land use data using a classifier list to create the standardization for land use utility categories include vacant and a subcategory classifier list with more specific uses related to the land use utility classifier list.

Step 4. Data associations with standardized data, parcel boundaries and property data as well as location boundary data used such as a city, MSA etc.;

Step 5. Build logic for queries this includes being able to return standardized land use utility at the parcel level and aggregating to one or more locations that account for one or more parcels.

Step 6. Logic for arbitrage opportunity uses standardization by parcel using zoning and/or future land use as compared to current property use. See Chart 5. Arbitrage opportunity can be identified by zoning and/or future land use. See Chart 6.

CHART 5 Standardized Standardized utility utility Optimal Arbitrage parcel Current use Zoned use Utility opportunity A-subject Residential Mixed-use Mixed-use T B Mixed-use Mixed-use Mixed-use F C Multifamily Mixed-use Mixed-use T

Step 7. The option to add a Confidence Score to outputs from Chart 5 include numeric and/or text—see Chart 6—and apply to the identified arbitrage opportunities.

CHART 6 Arbitrage Confidence Score opportunity Description example combines parcel type numeric and text A-subject zoning 75% confidence score is for locations with high levels of adjacent standardized land use utility and at parcel zoning arbitrage. B n/a n/a C zoning 75% confidence score is for locations with high levels of adjacent standardized land use utility and at parcel zoning arbitrage.

By standardizing “vacant land” at the core classifier list and subcategory classifier list, it is possible to find errors in how data records refer to this use and could be a way to check quality for current use data standardization by data aggregators of public record data.

An unexpected result from standardizing land use utility includes being able to track, search, assess, compare and visually understand land use supply, comparable locations, changes in the “true” market for a category, finding and searching for suitable locations with the novel idea of an index for standardized land use utility and pattern index.

Another unexpected result is the ability to measure impacts of land use over time and have historic references as well as the ability to use historic for future prediction models.

Yet another unexpected result is arbitrage opportunities by zoning and/or future land use. The market has not been able to clearly identify such nuances in an automated fashion until land use utility standardization. Furthermore, tracking changes in opportunities by location and/or over time have not been achievable prior to this invention.

Yet another unexpected result are improvements in flexibility of data outputs from system. As disclosed, data outputs can be spatial, tabular, vector tiles, hybrid like geoJSON and extensive visualizations including maps, charts, reports etc.

The above detailed description of embodiments of the invention is not intended to be exhaustive or to limit the invention to the precise form disclosed above. While specific embodiments of, and examples for, the invention are described above for illustrative purposes, various equivalent modifications are possible within the scope of the invention, as those skilled in the relevant art will recognize. For example, while steps are presented in a given order, alternative embodiments may perform routines having steps in a different order. The teachings of the invention provided herein can be applied to other systems, not only the systems described herein. The various embodiments described herein can be combined to provide further embodiments. These and other changes can be made to the invention in light of the detailed description.

All the above references and U.S. patents and applications are incorporated herein by reference. Aspects of the invention can be modified, if necessary, to employ the systems, functions and concepts of the various patents and applications described above to provide yet further embodiments of the invention.

These and other changes can be made to the invention in light of the above detailed description. In general, the terms used in the following claims, should not be construed to limit the invention to the specific embodiments disclosed in the specification, unless the above detailed description explicitly defines such terms. Accordingly, the actual scope of the invention encompasses the disclosed embodiments and all equivalent ways of practicing or implementing the invention under the claims.

While certain aspects of the invention are presented below in certain claim forms, the inventors contemplate the various aspects of the invention in any number of claim forms.

Items

The invention includes, but is not limited to various items as described herein.

Item 1. A method of transforming data to create a zoning score, the method comprising:

a) producing a zoning score having a value between 0 and 100 with the value reporting the amount to which a property: i. appears to be conforming to zoning code rules; ii. appears to possess upside potential, such as an increase in building size, less any downside risk, such as a four-plex property in an area zoned for a single family home only; b) obtaining data for a property to be considered; c) obtaining each development standard from each zoning code requirement affecting the property to be considered; d) transforming each zoning code requirement to a user defined weight ranging from 0 to 100%; e) creating a zoning score by multiplying each zoning code requirement by the user defined weight and a true (1) or false (0) outcome, with the outcome derived by comparing a zoning code requirement to the applicable characteristic of the property to be considered, with such characteristics comprising, minimum lot size, floor area ratio, density of lot, setback—front, setback—rear, setback—side, parking units, minimum lot width, maximum height of building in feet, maximum height of building in stories, and minimum lot size.

Item 2. A method of transforming real estate records to create a report of permitted land uses, the method comprising:

a. entering a search for a specific parcel of real property; b. retrieving a zoning code abbreviation applicable to the specific parcel of real property; c. transforming the zoning code abbreviation into a word phrase; d. displaying the zoning code and word phrase; e. retrieving data pertaining to the specific parcel of real property, the retrieved data to include, lot size, location, type of building, square footage of any building, pending projects, and proximity to a hazard, with the term hazard defined as a floodplain or moratorium use; and f. comparing the retrieved zoning code to the retrieved data pertaining to the specific parcel of real property and creating a report displaying permitted uses for the specific parcel of real property.

Item 3. A method of transforming data to create a zoning flexibility rating, the method comprising the assignment and summation of positive or negative values to the following data elements:

a) prominence of zoning codes for a location being rated; b) number of uses permitted within the location being rated; c) size related rules with an emphasis on density and FAR; d) exclusion and exemptions based, in part, upon the number other sections within a code referenced; e) parking requirements; f) design review requirements; g) rebuilding options in the event of property destruction; f) bonus densities; g) energy related controls; and h) moratorium uses and encouraged uses.

Item 4. A computer implemented method to automatically identify real property zoning and/or land-use development controls with property data for one or more parcels comprising the steps of:

a) importing geographical information system spatial data into a database including lots and parcels, city and/or county boundaries, zoning for base and/or overlay districts, and/or planning areas in order to associate zoning and/or land-use development controls on one or more parcels; b) transferring or importing data for zoning districts and/or land-use development control document and/or planning documents into a database comprising district abbreviation, district name, descriptor and department and/or agency associated with the data; c) automatically obtaining data for real property and importing the data into a database; d) entering a search comprising one or more methods using keyboard, geo location function, images or map with desired search criteria comprising location, zoning district, planning area; district descriptor and/or agency; e) retrieving data comprising development controls for a location, district name and/or abbreviation with property data for one or more properties in one or more jurisdictions; and f) creating an output comprising electronic map, report, API.

Item 5. A computer implemented method of identifying existing conditions for a study area comprising the steps of:

a) importing or transferring data associated with changes for a location comprising demographics, neighborhood boundaries, zoning permits, zoning cases, building permits, retail sales, real estate sales into a database b) entering a search by identifying the study area comprising drawing on a map, entering a zip code, entering locations; and c) retrieving results comprising number of parcels, length of ownership, existing uses, age of buildings, demographics, assessed values

Item 6. A computer implemented method to automatically identify real property locations based on development potential and/or use using structured data for real property zoning controls comprising the steps of:

a) importing geographical information system spatial data into a database comprising lots and parcels, city and/or county boundaries, zoning base and/or overlay, planning areas in order to associate current zoning controls on one or more parcels; b) automatically obtaining data for real property and importing the data into a database; c) importing or transferring zoning controls into database comprising district development standards related to individual zoning district basis into a database comprising of lot size and dimensions, floor area ratio, density of units, height requirements, yards or setbacks, parking and/or regulated uses with permissions individual zoning districts into a database comprising identified uses associating each with a permission as identified by the specific zoning district within the jurisdiction such as “permitted” “conditional” and “prohibited”; e) entering search comprising one or more jurisdictions, locations, zoning districts, uses regulated by zoning, development potential building size and search criteria for lot size parameters and/or existing building size range and/or use and/or age; f) retrieving data matching search criteria comprising one or more properties, districts, uses or jurisdictions the retrieved data to include development standards regarding the physical attributes of how the property might be developed using the parcel's zoning and use permissions comprising of a name of a use and the restriction or permission regarding how the property might be used as it relates to a business, a building type and/or activity; and g) creating an output comprising data, API, report, inquiry, visual display of data returns on electronic map.

Item 7. A computer implemented method to identify suitable properties by applying logic for zoning compatibility issues, the method comprising:

a) importing geographical information system spatial data into a database comprising lots and parcels, city and/or county boundaries and/or Extra Territorial Jurisdiction boundaries, zoning base and/or overlay districts, street type, planning areas in order to associate zoning controls on one or more parcels; b) importing data to identify protected uses and/or areas associated with compatibility comprising schools, churches, residential neighborhoods; c) transferring rules expressing compatibility requirements comprising buffer areas, distance-to rules, adjacency use, nearby uses, adjacency zoning district(s), street type by identifying zoning districts and/or planning areas into structured database with IF/THEN statements articulating conditions; d) applying rule logic to run queries on a structured database to identify locations with or without zoning compatibility constraints; e) retrieving data matching criteria; and f) creating an output comprising data, API, report, visual display of data returns on electronic map

Item 8. A computer implemented method to automatically identify and manipulate hypothetical real property development potential and/or use through structured data of real property zoning controls comprising the steps of:

a) importing geographical information system spatial data into a database comprising lots and parcels, city and/or county boundaries and/or Extra Territorial Jurisdiction boundaries, zoning base and/or overlay districts, planning areas in order to associate zoning controls on one or more parcels or part of a parcel basis; b) automatically obtaining data for real property and importing the data into a database; c) importing or transferring zoning controls into database comprising district development standards related to individual zoning district basis into a database comprising of lot size and dimensions, floor area ratio, density of units, height requirements, yards or setbacks, parking and/or permissions about uses related to individual zoning districts into a database comprising of the list of identified business uses, building types, activities and associating each with a permission as identified by the specific zoning district within the jurisdiction such as “permitted” “conditional” and “prohibited”; e) creating a search that identifies one or more properties to run a hypothetical query; f) selecting a hypothetical zoning action comprising rezoning, changing a use permission, changing a development standard and then allows for selecting one or more variables to test outcomes based on the hypothetical situation comprising a zoning district, a permission for a use, a value for specified development standard; f) retrieving data pertaining to search criteria the retrieved data to include a comparison of existing zoning controls, for the one or more properties identified in the search, to outcomes for the hypothetical scenario in regard to reflect how the property (or properties) might be developed and/or used; and g) creating an output comprising data, API, report, visual display of data returns on electronic map.

Item 9. A computer implemented method to automatically create a capacity study comprising the steps of:

a) identifying the area for a planning capacity study; b) applying hypothetical zoning action comprising rezoning, change of use permission and/or change of a development standard; c) retrieving data pertaining to search criteria the retrieved data to include a comparison of existing zoning controls for the planning capacity study area identified in the search, to outcomes for the hypothetical scenario in regard to reflect how the properties might be developed and/or used; d) filtering data returns to comprise removal of districts and/or existing uses to iterate results; and f) creating an output comprising data, API.

Item 10. A computer implemented method to automatically create descriptors for changes for a plurality of zoning ordinances comprising the steps of:

a) automatically, by computer, importing GIS spatial data into a database comprising lots and parcels, zoning for base and/or overlay districts for an identified jurisdiction or municipality with date of data; b) manually mapping data elements from the GIS and creating an association with the corresponding key descriptors such as name, definition or focus for each zoning district from the specific jurisdiction zoning ordinance; c) automatically, by computer, assigning one or more descriptors to each zoning district, from lists of descriptors for properties including zoning district type, land-use/development focus, and/or land-use/development policy focus, wherein a list of descriptors for zoning district type includes one or more of base, overlay, or other, a list of descriptors for land-use/development focus includes one or more of residential, multifamily, mixed-use, commercial, industrial, agricultural, public, planned special or unzoned, a list of descriptors for land-use/development policy focus includes one or more of design, pedestrian friendly, medical/health, entertainment, developer incentive, preservation, or transit related, and the importing, transferring and assigning produce a logic-enhanced database; d) automatically, by computer, intersecting GIS spatial data for zoning with parcels to create a baseline time period showing zoning descriptor data for a specific jurisdiction with a breakdown of parcels by descriptor; e) automatically, by computer, importing current spatial data as listed in previous Step a) for importing GIS data with updated date of data; f) automatically, by computer, assigning one or more descriptors to each zoning district previously noted in Step c); g) automatically, by computer, performing a search of the logic-enhanced database for one or more descriptors; 8) manually mapping one or more descriptors from a list of descriptors regarding the zoning change using publicly available data, wherein a list of descriptors for who initiated the change: owner, city or other, a list of descriptors for why the change was made: revitalization area, comprehensive plan, housing need, preservation protection, and/or other, and the importing, transferring, adding and assigning produce a logic-enhanced database; 9) automatically, by computer, performing a search of the logic-enhanced database for one or more descriptors; 10) automatically, by computer, creating an output of data.

Steps Necessary to Create Zoning Complexity Index

a) lots and parcels, zoning for base and/or overlay districts for an identified jurisdiction or municipality with date of data; b) manually mapping data elements from the GIS and creating an association with the corresponding key descriptors such as name, definition or focus for each zoning district from the specific jurisdiction zoning ordinance; c) automatically, by computer, assigning one or more descriptors to each zoning district, from lists of descriptors for properties including zoning district type, land-use/development focus, and/or land-use/development policy focus, wherein a list of descriptors for zoning district type includes one or more of base, overlay, or other, a list of descriptors for land-use/development focus includes one or more of residential, multifamily, mixed-use, commercial, industrial, agricultural, public, planned special or unzoned, a list of descriptors for land-use/development policy focus includes one or more of design, pedestrian friendly, medical/health, entertainment, developer incentive, preservation, or transit related, and the importing, transferring and assigning produce a logic-enhanced database; d) automatically, by computer, intersecting GIS spatial data for zoning with parcels to create a baseline time period showing zoning descriptor data for a specific jurisdiction with a breakdown of parcels by descriptor; e) automatically, by computer, importing current spatial data as listed in Step 1 with updated date of data; f) automatically, by computer, assigning one or more descriptors to each zoning district as noted in Step c). g) automatically, by computer, retrieving a list of parcels identified as meeting criteria of change by time period; h) automatically, by computer, comparing base line numbers of parcels by zoning district type and land-use/development focus to those retrieved in Step g); i) automatically, by computer, creating an output of data.

Item A. A system for automating land use utility through standardizing, comprising:

A graphical user interface receiving request from a user for a land use utility query, including identifying one or more of the following: locations, land use utility categories, arbitrage opportunities and/or dates; A database storage for a plurality of data stores comprising zoning and/or future land use, parcel boundaries and property data with a date to represent the related dataset date; A processor receiving data from data stores and executing instructions to standardize a plurality of data sets using a land use utility classifier list wherein processor standardizes one or more zoning districts and/or future land use designators, corresponding to one or more parcels, to one or more classifiers from the land use utility classifier list, wherein processor standardizes one or more fields for current property use, corresponding to one or more property data records associated with one or more parcels, to one or more classifiers from the land use utility classifier list, and the processor generating results for standardized land use utility query from logic enhanced database; and The graphical interface user receiving results from the processor for requested standardized land use utility query and displaying resulting outputs of data and/or one or more visualizations of data.

Item B. A method for automating land use utility through standardizing, comprising:

Receiving through a graphical user interface, a request identifying a land use utility query, including identifying one or more of the following: locations, land use utility categories, arbitrage opportunities and/or dates; Accessing a plurality of data stores storing data comprising zoning and/or future land use, parcel boundaries and property data with a date to represent the related dataset date; Executing, by processor, instructions to standardize plurality of data sets from plurality of data stores using a land use utility classifier list wherein processor standardizes one or more zoning districts and/or future land use designators, corresponding to one or more parcels, to one or more classifiers from a land use utility classifier list, wherein processor standardizes one or more fields for current property use, corresponding to one or more property data records associated with one or more parcels, to one or more classifiers from a land use utility classifier list, and the processor generating results for standardized land use utility query from logic enhanced database, and Displaying, on the graphical user interface, the response to the query for standardized land use utility wherein results are an output of data and/or one of more visualizations. 

What I claim as my invention is:
 1. A system for automating land use utility through standardizing, comprising: A graphical user interface receiving request from a user for a land use utility query, including identifying one or more of the following: locations, land use utility categories, arbitrage opportunities and/or dates; A database storage for a plurality of data stores comprising zoning and/or future land use, parcel boundaries and property data with a date to represent the related dataset date; A processor receiving data from data stores and executing instructions to standardize a plurality of data sets using a land use utility classifier list wherein processor standardizes one or more zoning districts and/or future land use designators, corresponding to one or more parcels, to one or more classifiers from the land use utility classifier list, wherein processor standardizes one or more fields for current property use, corresponding to one or more property data records associated with one or more parcels, to one or more classifiers from the land use utility classifier list, and the processor generating results for standardized land use utility query from logic enhanced database; and The graphical interface user receiving results from the processor for requested standardized land use utility query and displaying resulting outputs of data and/or one of more visualizations of data.
 2. The system of claim 1, wherein Location can include any type of geographical area defined by a boundary including but not limited to: one or more parcels, places, cities, MSAs and/or economically defined areas and/or drawn area, Land use utility categories contains one or more category names recognized in real estate to convey residential, multifamily, commercial, mixed use, vacant etc., and arbitrage opportunities are identified by standardized land use utility by data source wherein data source is property data, zoning data and/or future land data, and Dates reflect time by any recognizable representation.
 3. The system of claim 1, wherein the processor further modifies standardized land use utility classifier to include a hierarchy ranking and subcategory list wherein Hierarchy ranking for intensity of standardized land use category and ranks agricultural at the low end and industrial is at the high end, and Land use utility classifier list includes a subcategory list further standardize zoning and/or future land use and current property use to subcategories including one or more of the following: duplex, triplex, fourplex, townhouse, manufactured house, garden style apartment, accessory uses, hotel, drive through, self-storage facility, gas station, parking, medical office, live/work, heavy manufacturing, research campus and/or lab, cold storage.
 4. The system of claim 3, wherein the processor further determines property type per real estate lending standards wherein loan conformity standards for residential loans are one to four (1-4) dwelling units and for commercial loans are multifamily five or more (5+) dwelling units and/or nonresidential property.
 5. The system of claim 1, wherein the graphical user interface provides to the processor an input for preset or customizable options wherein Preset options location identifiers, standardized land use utility categories, opportunities and/or time, and Customizable include preset options plus the option to include other related data in the query wherein related data comprises one or more: property records, transportation data and/or environmental data that impacts property development such as flood hazards, endangered species areas.
 6. The system of claim 1, wherein the processor further determines an arbitrage opportunity wherein Opportunity is identified for one or more parcels when a more intense standardized land use utility difference is identified using standardized land use intensity hierarchy between standardized current property use and standardized zoning on a parcel and/or when a difference is identified between standardized zoning and standardized future land use, An arbitrage opportunity can be identified as zoning or future land use related, and The concentration of arbitrage opportunities can be expressed for one or more locations by a weighted system wherein the quantity of arbitrage opportunities is measured by one or more larger geographies including: neighborhood, zip, city and/or MSA.
 7. The system of claim 6, wherein the processor further determines an optimal standardized land use utility with optional confidence score related to the likelihood of achieving optimal standardized land use utility wherein The likelihood is weighed by comparing identified arbitrage opportunity parcel to standardized land use utility for one or more nearby parcels, Option to include in the weighing other property attributes related to the one or more locations expressing one or more: density, ownership, building age, building footprint, building height and/or size, and Optimal standardized land use utility is expressed by standardized land use category and optional confidence score is expressed as a numeric value either single or range and/or related textual qualitative description.
 8. The system of claim 1, wherein the processor further determines standardized land use utility patterns for one more locations wherein land use utility is measured and/or tracked by standardized current property use and standardized zoning and/or future land use with census data at corresponding geographical level to identify number of households and/or other demographics with the option of including property characteristic related to the one or more locations expressing one or more: density, ownership, building age, building footprint, building height and/or size.
 9. The system of claim 8, wherein the processor further tracks changes over time in number of households and/or other demographics with the option of including property characteristic related to the one or more locations expressing a density and/or size by standardized current property use and standardized zoning and/or future land use for one or more locations to create a standardized land use utility pattern index.
 10. The system of claim 1, wherein outputs can include one or more of the following: data, visualization and/or report wherein data can be exportable in one or more of the following: spatial, tabular, geometry-oriented and/or hybrid data format, wherein data can be visualized in one or more of the following: an electronic map, map layer or layers, vector tiles, chart and/or dashboard, wherein visualization can include the optional use of icons associated with one or more land use utility categories and wherein report can be in an electronic form or printable for a report use.
 11. A method for automating land use utility through standardizing, comprising: Receiving through a graphical user interface, a request identifying a land use utility query, including identifying one or more of the following: locations, land use utility categories, arbitrage opportunities and/or dates; Accessing a plurality of data stores storing data comprising zoning and/or future land use, parcel boundaries and property data with a date to represent the data date; Executing, by processor, instructions to standardize plurality of data sets from plurality of data stores using a land use utility classifier list wherein processor standardizes one or more zoning districts and/or future land use designators, corresponding to one or more parcels, to one or more classifiers from a land use utility classifier list, wherein processor standardizes one or more fields for current property use, corresponding to one or more property data records associated with one or more parcels, to one or more classifiers from a land use utility classifier list, and the processor generating results for standardized land use utility query from logic enhanced database; and Displaying, on the graphical user interface, the response to the query for standardized land use utility wherein results are an output of data and/or one of more visualizations.
 12. The method of claim 11 wherein Location can include any type of geographical area defined by a boundary including but not limited to: one or more parcels, places, cities, MSAs and/or economically defined areas and/or drawn area, Land use utility categories contains one or more category names recognized in real estate to convey residential, multifamily, commercial, mixed use, vacant etc., and arbitrage opportunities are identified by standardized land use utility by data source wherein data source is property data, zoning data and/or future land data, and Dates reflect time by any recognizable representation.
 13. The method of claim 12 wherein the processor further modifies standardized land use utility classifier to include a hierarchy ranking and subcategory list wherein, Hierarchy ranking for intensity of standardized land use category and ranks agricultural at the low end and industrial is at the high end, and Land use utility classifier list includes a subcategory list further standardize zoning and/or future land use and current property use to subcategories including one or more of the following: duplex, triplex, fourplex, townhouse, manufactured house, garden style apartment, accessory uses, hotel, drive through, self-storage facility, gas station, parking, medical office, live/work, heavy manufacturing, research campus and/or lab, cold storage.
 14. The method of claim 13 wherein the processor further determines property type per real estate lending standards wherein loan conformity standards for residential loans are one to four (1-4) dwelling units and for commercial loans are multifamily five or more (5+) dwelling units and/or nonresidential property.
 15. The method of claim 11 wherein the graphical user interface provides to the processor an input for preset or customizable options wherein Preset options location identifiers, standardized land use utility categories, opportunities and/or time, and Customizable include preset options plus the option to include other related data in the query wherein related data comprises one or more: property records, transportation data and/or environmental data that impacts property development such as flood hazards, endangered species areas.
 16. The method of claim 11 wherein the processor further determines an arbitrage opportunity wherein Opportunity is identified for one or more parcels when a more intense standardized land use utility difference is identified using standardized land use intensity hierarchy between standardized current property use and standardized zoning on a parcel and/or when a difference is identified between standardized zoning and standardized future land use, An arbitrage opportunity can be identified as zoning or future land use related, and The concentration of arbitrage opportunities can be expressed for one or more locations by a weighted system wherein the quantity of arbitrage opportunities is measured by one or more larger geographies including: neighborhood, zip, city and/or MSA.
 17. The method of claim 16 wherein the processor further determines an optimal standardized land use utility with optional confidence score related to the likelihood of achieving optimal standardized land use utility wherein The likelihood is weighed by comparing identified arbitrage opportunity parcel to standardized land use utility for one or more nearby parcels, Option to include in the weighing other property attributes related to the one or more locations expressing one or more: density, ownership, building age, building footprint, building height and/or size, and Optimal standardized land use utility is expressed by standardized land use category and optional confidence score is expressed as a numeric value either single or range and/or related textual qualitative description.
 18. The method of claim 11 wherein the processor further determines standardized land use utility patterns for one more locations wherein land use utility is measured and/or tracked by standardized current property use and standardized zoning and/or future land use with census data at corresponding geographical level to identify number of households and/or other demographics with the option of including property characteristic related to the one or more locations expressing one or more: density, ownership, building age, building footprint, building height and/or size.
 19. The method of claim 18, wherein the processor further tracks changes over time in number of households and/or other demographics with the option of including property characteristic related to the one or more locations expressing a density and/or size by standardized current property use and standardized zoning and/or future land use for one or more locations to create a standardized land use utility pattern index.
 20. The method of claim 11, wherein outputs can include one or more of the following: data, visualization and/or report wherein data can be exportable in one or more of the following: spatial, tabular, geometry-oriented and/or hybrid data format, wherein data can be visualized in one or more of the following: an electronic map, map layer or layers, vector tiles, chart and/or dashboard, wherein visualization can include the optional use of icons associated with one or more land use utility categories and wherein report can be in an electronic form or printable for a report use. 